EP2867889A2 - Méthodes et systèmes de gestion de données d'adaptation - Google Patents

Méthodes et systèmes de gestion de données d'adaptation

Info

Publication number
EP2867889A2
EP2867889A2 EP13808577.4A EP13808577A EP2867889A2 EP 2867889 A2 EP2867889 A2 EP 2867889A2 EP 13808577 A EP13808577 A EP 13808577A EP 2867889 A2 EP2867889 A2 EP 2867889A2
Authority
EP
European Patent Office
Prior art keywords
speech
adaptation data
facilitated transaction
result
particular party
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP13808577.4A
Other languages
German (de)
English (en)
Other versions
EP2867889A4 (fr
Inventor
Royce A. Levien
Richard T. Lord
Robert W. Lord
Mark A. Malamud
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Elwha LLC
Original Assignee
Elwha LLC
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US13/538,866 external-priority patent/US20130325447A1/en
Priority claimed from US13/538,855 external-priority patent/US9495966B2/en
Priority claimed from US13/564,647 external-priority patent/US9620128B2/en
Priority claimed from US13/564,650 external-priority patent/US20130325449A1/en
Priority claimed from US13/609,142 external-priority patent/US20130325451A1/en
Priority claimed from US13/609,143 external-priority patent/US9305565B2/en
Priority claimed from US13/609,139 external-priority patent/US10431235B2/en
Priority claimed from US13/662,228 external-priority patent/US10395672B2/en
Application filed by Elwha LLC filed Critical Elwha LLC
Publication of EP2867889A2 publication Critical patent/EP2867889A2/fr
Publication of EP2867889A4 publication Critical patent/EP2867889A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/28—Constructional details of speech recognition systems
    • G10L15/30—Distributed recognition, e.g. in client-server systems, for mobile phones or network applications
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/065—Adaptation
    • G10L15/07—Adaptation to the speaker
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/06—Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/065—Adaptation
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/226—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics
    • G10L2015/227—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics of the speaker; Human-factor methodology
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G10L2015/226—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics
    • G10L2015/228—Procedures used during a speech recognition process, e.g. man-machine dialogue using non-speech characteristics of application context

Definitions

  • the present application is related to and/or claims the benefit of the earliest available effective filing date(s) from the following listed application(s) (the "Priority Applications"), if any, listed below (e.g., claims earliest available priority dates for other than provisional patent applications or claims benefits under 35 USC ⁇ 119(e) for provisional patent applications, for any and all parent, grandparent, great-grandparent, etc. applications of the Priority Application(s)).
  • the present application is related to the "Related Applications,” if any, listed below.
  • ADAPTATION DATA naming Royce A. Levies, Richard TNase Lord, Robert W. Lord, ami Mark A. Malamud as inventors, filed 29 June 2012 with attorney docket no. 0711- 003-042-C00001, which is currently co-pending or is an application of which a currently co-pending application is entitled to the benefit of the filing date, and which is a continuation of United States Patent Application No. 13/538,855, entitled SPEECH RECOGNITION ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Leviesi, Richard T. Lord, Robert W. Lord, and Mark A. Malamud as inventors, filed 29 June 2012 with attorney docket no. 0711-003-042-000000.
  • ADAPTATION DATA naming Royce A. Leviesi, Richard T. Lord, Robert W, Lord, and Mark A. Malamud as inventors, filed 1 August 2012 with attorney docket no. 0711-003-043-C00001, which is currently co-pending or is an application of which a currently co-pending application is entitled to the benefit of the filing date, and which is a continuation of United States Patent Application No. 13/564,647, entitled SPEECH RECOGNITION ADAPTATION SYSTEMS BASED ON ADAPTATION DATA, naming Royce A. Leviesi, Richard T. Lord, Robert W. Lord, asid Mark A. Malamud as inventors, filed I August 2012 with attorney docket no. ⁇ 711-0 ⁇ 3-043- ⁇ 0 ⁇ .
  • ADAPTATION DATA naming Royce A duplicate Leviesi, Richard T. Lord, Robert W. Lord, and Mark A. Malamud as inventors, filed 1 August 2012 with attorney docket no. 0711-003-044-0)0001, which is currently co-pending or is an application of which a currently co-pending application is entitled to the benefit of the filing date, and which is a continuation of United States Patent Application No. 13/564,650, entitled SPEECH RECOGNITION ADAPTATION SYSTEMS BASED ON ADAPTATION DATA,
  • Applicant has provided above a specific reference to the application(s) from which priority is being claimed as recited by statute. Applicant understands that the statute is unambiguous in its specific reference language and does not require either a serial number or any characterization, such as "continuation” or “continuation-in-part,” for claiming priority to U.S. patent applications.
  • a method includes but is not limited to managing adaptation data, wherein the adaptation data is correlated to at least one aspect of speech of a particular party, facilitating transmission of the adaptation data to a target device, in response to an indicator related to a speech-facilitated transaction of a particular party, wherein the adaptation data is correlated to at least one a spect of speech of the particular party, and determining whether to update the adaptation data, said determination at least partly based on a result of at least a portion of the speech- facil itated transaction.
  • the logical operations/functions described herein are a distillation of machine specifications or other physical mechanisms specified by the operations/functions such that the otherwise inscrutable machine specifications may be comprehensible to the human mind.
  • the distillation also allows one of skill in the art to adapt the
  • VHDL Very high speed Hardware Description Language
  • a high-level programming language is a programming language with strong abstraction, e.g., multiple levels of abstraction, from the details of the sequential organizations, states, inputs, outputs, etc., of the machines that a high-level programming language actually specifies. See, e.g. , Wikipedia, High-level programming language, http://en.wMpedia.org/wi]d/ffi ⁇ (as of June 5,
  • the hardware used in the computational machines typically consists of some type of ordered matter (e.g., traditional electronic devices (e.g., transistors), deoxyribonucleic acid (DNA), quantum devices, mechanical switches, optics, fluidics, pneumatics, optical devices (e.g., optical interference devices), molecules, etc.) that are arranged to form logic gates.
  • Logic gates are typically physical devices that, may be electrically, mechanically, chemically, or otherwise driven to change physical state in order to create a physical reality of Boolean logic.
  • Logic gates may be arranged to form logic circuits, which are typically physical devices that may be electrically, mechanically, chemically, or otherwise driven to create a physical reality of certain logical functions.
  • Types of logic circuits include such devices as multiplexers, registers, arithmetic logic units (ALUs), computer memory, etc., each type of which may be combined to form yet other types of physical devices, such as a central processing unit (CPU) ⁇ the best known of which is the microprocessor.
  • CPU central processing unit
  • a modern microprocessor will often contain more than one hundred million logic gates in its many logic circuits (and often more than a billion transistors). See, e.g., Wikipedia, Logic gates, http://en.wikipedia.org/wiki/Logic ... gates (as of June 5, 2012, 21 :03 GMT),
  • the logic circuits forming the microprocessor are arranged to provide a microarchitecture that will carry out the instructions defined by that microprocessor's defined Instruction Set Architecture.
  • the Instruction Set Architecture is the part of the microprocessor architecture related to programming, including the native data types, instructions, registers, addressing modes, memory architecture, interrupt and exception handling, and external Input/Output, See, e.g., Wikipedia, Computer architecture, http://en.wikipedia.orgAviki/Computer_architeeture (as of June 5, 2012, 21 :03 GMT).
  • the Instruction Set Architecture includes a specification of the machine language that can be used by programmers to use/control the microprocessor. Since the machine language instructions are such that they may be executed directly by the microprocessor, typically they consist of strings of binary digits, or bits. For example, a typical machine language instruction might be many bits long (e.g., 32, 64, or 128 bit strings are currently common). A typical machine language instruction might take the form "1 1 1 100001010 1 1 100001 1 1100111 111" (a 32 bit instruction).
  • the binary number "1" (e.g., logical "1") in a machine language instruction specifies around +5 volts applied to a specific "wire” (e.g., metallic traces on a printed circuit board) and the binary number "0" (e.g., logical "0") in a machine language instruction specifies around -5 volts applied to a specific "wire.”
  • a specific "wire” e.g., metallic traces on a printed circuit board
  • the binary number "0" (e.g., logical "0") in a machine language instruction specifies around -5 volts applied to a specific "wire.”
  • machine language instructions also select out and activate specific groupings of l ogic gates from the millions of logic gates of the more general machine.
  • machine language instruction programs even though written as a string of zeros and ones, specify many, many constructed physical machines or physical machine states.
  • Machine language is typically incomprehensible by most humans (e.g., the above example was just ONE instruction, and some personal computers execute more than two billion instructions every second). See, e.g., Wikipedia, Instructions per second, http://en.wikipedia.org/wiki/Instmctions___p (as of June 5, 2012, 21 :04 GMT).
  • a compiler is a device that takes a statement that is more comprehensible to a human than either machine or assembly language, such as "add 2 + 2 and output the result," and translates that human understandable statement into a complicated, tedious, and immense machine language code (e.g., millions of 32, 64, or 128 bit length strings). Compilers thus translate high-level programming language into machine language.
  • any such operational/functional technical descriptions - in view of the disclosures herein and the knowledge of those skilled in the art - may be understood as operations made into physical reality by (a) one or more interchained physical machines, (b) interchained logic gates configured to create one or more physical rnachine(s) representative of sequential/combinatorial logic(s), (c) interchained ordered matter making up logic gates (e.g., interchained electronic devices (e.g., transistors), DNA, quantum devices, mechanical switches, optics, fluidics, pneumatics, molecules, etc.) that create physical reality representative of logic(s), or (d) virtually any combination of the foregoing.
  • logic gates e.g., interchained electronic devices (e.g., transistors), DNA, quantum devices, mechanical switches, optics, fluidics, pneumatics, molecules, etc.
  • any physical object which has a stable, measurable, and changeable state may be used to construct a machine based on the above technical description.
  • Charles Babbage for example, constructed the first computer out of wood and powered by cranking a handle.
  • the logical operations/functions set, forth in the present technical description are representative of static or sequenced specifications of various ordered-matter elements, in order that such specifications may be comprehensible to the human mind and adaptable to create many various hardware configurations.
  • the logical operations/functions disclosed herein should be treated as such, and should not be disparagingly characterized as abstract ideas merely because the specifications they represent are presented in a manner that one of skill in the art can readily understand and apply in a manner independent of a specific vendor's hardware implementation,
  • one or more related systems may be any one or more related systems.
  • the one or more related systems may include, but are not limited to, circuitry and/or programming for effecting the
  • circuitry and/or programming may be virtually any combination of hardware, software, and/or firmware configured to effect the herein- referenced method aspects depending upon the design choices of the system designer, and limited to patentable subject matter under 35 USC 101 ,
  • a system includes, but is not limited to, means for managing adaptation data, wherein the adaptation data is correlated to at least one aspect of speech of a particular party, means for facilitating transmission of the adaptation data to a target device, in response to an indicator related to a speech- facilitated transaction of a particular party, wherein the adaptation data is correlated to at least one aspect of speech of the particular party, and means for determining whether to update the adaptation data, said determination at least partly based on a result of at least a portion of the speech-facilitated transaction.
  • a system includes, but is not limited to, circuitry for managing adaptation data, wherein the adaptation data is correlated to at least one aspect of speech of a particular party, circuitry for facilitating transmission of the adaptation data to a target device, in response to an indicator related to a speech- facilitated transaction of a particular party, wherein the adaptation data is correlated to at least one aspect of speech of the particular party, and circuitry for determining whether to update the adaptation data, said determination at least partly based on a result of at least a portion of the speech-facilitated transaction.
  • a computer program product comprising a signal hearing medium, bearing one or more instructions including, but not limited to, one or more instructions for managing adaptation data, wherein the adaptation data is correlated to at least one aspect of speech of a particular party, one or more instructions for facilitating transmission of the adaptation data, to a target device, in response to an indicator related to a speech-facilitated transaction of a particular party, wherein the adaptation data is correlated to at least one aspect of speech of the particular party, and one or more instructions for determining whether to update the adaptation data, said determination at least partly based on a. result of at least a portion of the speech- facilitated transaction.
  • a device is defined by a computational language, such that the device comprises one or more interchained physical machines ordered for managing adaptation data, wherein the adaptation data is correlated to at least one aspect of speech of a particular party, one or more interchained physical machines ordered for facilitating transmission of the adaptation data to a target, device, in response to an indicator related to a speech-facilitated transaction of a particular party, wherein the adaptation data is correlated to at least one aspect of speech of the particular party, and one or more interchained physical machines ordered for determining whether to update the adaptation data, said determination at least partly based on a result of at least a portion of the speech-facilitated transaction.
  • FIG. 1A shows a high-level block diagram of an exemplary environment 100, according to an embodiment.
  • Fig. IB shows a high-level block diagram of a personal device 120 operating in an exemplary embodiment 100, according to an embodiment.
  • FIG. 1C shows a high-level diagram of an exemplary environment 100', which is an example of an exemplary embodiment 100 having a personal device 120, according to an embodiment.
  • Fig, ID shows a high-level diagram of an exemplary environment 100", which is an example of an exemplary embodiment 100 having a personal device 120, according to an embodiment.
  • Fig. IE shows a high-level diagram of an exemplar ⁇ ' environment 100" ', which is an example of an exemplary embodiment 100 having a personal device 120, according to an embodiment.
  • Fig, 2 including Figs. 2A-2B, shows a particular perspective of the speech adaptation data related to at least one aspect, of a particular party regulating module 152 of the personal device 120 of environment 100 of Fig. I B.
  • Fig, 3 shows a particular perspective of the speech adaptation data transmission, in response to indication of a speech- facilitated transaction including a target device, facilitating module 154 of the personal device 120 of environment 100 of Fig. IB.
  • Fig, 4 including Figs. 4A-4I, shows a particular perspective of speech adaptation data potential modification determining partly based on result of portion of speech-facilitated transaction module 156 of the personal device 120 of environment 100 of Fig. IB.
  • Fig, 5 is a high-level logic flowchart of a process, e.g., operational flow 500, according to an embodiment.
  • F g, 6A is a high-level logic flow chart of a process depicting alternate implementations of a managing adaptation data operation 502 of Fig, 5, according to one or more embodiments.
  • Fig. 6B is a high-level logic flow chart of a process depicting alternate implementations of a managing adaptation data operation 502 of Fig. 5, according to one or more embodiments.
  • F g, 7 A is a high-level logic flow chart of a process depicting alternate implementations of a facilitating transmission of the adaptation data 504 of Fig. 5, according to one or more embodiments.
  • Fig. 7B is a high-level logic flow chart of a process depicting alternate implementations of a facilitating transmission of the adaptation data 504 of Fig. 5, according to one or more embodiments.
  • F g, 7C is a high-level logic flow chart of a process depicting alternate implementations of a facilitating transmission of the adaptation data 504 of Fig. 5, according to one or more embodiments.
  • Fig. 7D is a high-level logic flow chart of a process depicting alternate implementations of a facilitating transmission of the adaptation data 504 of Fig. 5, according to one or more embodiments.
  • F g, 8A is a high-level logic flowchart of a process depicting determining whether to update the adaptation data 506 of Fig. 5, according to one or more embodiments.
  • Fig, 8B is a high-level logic flowchart of a process depicting determining whether to update the adaptation data 506 of Fig. 5, according to one or more embodiments.
  • Fig. 8C is a high-level logic flowchart of a process depicting determining whether to update the adaptation data 506 of Fig. 5, according to one or more embodiments.
  • Fig, 8D is a high-level logic flowchart of a process depicting determining whether to update the adaptation data 506 of Fig. 5, according to one or more embodiments.
  • Fig. 8E is a high-level logic flowchart of a process depicting determining whether to update the adaptation data 506 of Fig. 5, according to one or more embodiments.
  • F g, 8F is a high-level logic flowchart of a process depicting determining whether to update the adaptation data 506 of Fig. 5, according to one or more embodiments.
  • Fig. 8G is a high-level logic flowchart of a process depicting determining whether to update the adaptation data 506 of Fig. 5, according to one or more embodiments.
  • F g, 8H is a high-level logic flowchart of a process depicting determining whether to update the adaptation data 506 of Fig. 5, according to one or more embodiments.
  • Fig. 81 is a high-level logic flowchart of a process depicting determining whether to update the adaptation data 506 of Fig. 5, according to one or more embodiments.
  • computationally implemented methods, systems, circuitry, articles of manufacture, ordered chains of matter, and computer program products are designed to, among other things, provide an interface for managing adaptation data, wherein the adaptation data is correlated to at least one aspect of speech of a particular party, facilitating transmission of the adaptation data to a target device, in response to an indicator related to a speech-facilitated transaction of a particular party, wherein the adaptation data is correlated to at least one aspect of speech of the particular party, and determining whether to update the adaptation data, said determination at least partly based on a result of at least a portion of the speech- facilitated transaction.
  • the present application uses formal outline headings for clarity of presentation.
  • the outline headings are for presentation purposes, and that different types of subject matter may be discussed fhroughout the application (e.g., device(s)/structure(s) may e described under process(es)/operations heading(s) and/or process(es)/operations may be discussed under structure(s)/process(es) headings; and/or descriptions of single topics may span two or more topic headings).
  • the use of the formal outline headings is not intended to be in any way limiting.
  • user 105 is shown/described herein as a single illustrated figure, those skilled in the art, will appreciate that, user 105 may be representative of a human user, a robotic user (e.g., computational entity), and/or substantially any combination thereof (e.g., a user may be assisted by one or more robotic agents) unless context dictates otherwise.
  • a robotic user e.g., computational entity
  • substantially any combination thereof e.g., a user may be assisted by one or more robotic agents
  • Those skilled in the art will appreciate that, in general, the same may be said of "sender” and/or other entity-oriented terms as such terms are used herein unless context dictates otherwise.
  • an implementer may opt for a mainly hardware and/or firmware vehicle: alternatively, if flexibility is paramount, the implementer may opt for a mainly software implementation; or, yet again alternatively, the implementer may opt for some combination of hardware, software, and ' or firmware in one or more machines, compositions of matter, and articles of manufacture, limited to patentable subject matter under 35 USC 101.
  • logic and similar implementations may include software or other control structures.
  • Electronic circuitry may have one or more paths of electrical current constructed and arranged to implement various functions as described herein.
  • one or more media may be configured to bear a device-detectable implementation when such media hold or transmit device detectable instructions operable to perform as described herein.
  • implementations may include an update or modification of existing software or firm ware, or of gate arrays or programmabl e hardware, such as by performing a reception of or a transmission of one or more instructions in relation to one or more operations described herein.
  • an implementation may include special-purpose hardware, software, firmware components, and/or general-purpose components executing or otherwise invoking special-purpose components. Specifications or other implementations may be transmitted by one or more instances of tangible transmission media as described herein, optionally by packet transmission or otherwise by passing through distributed media at various times. [0075] Alternatively or additionally, implementations may include executing a special- purpose instruction sequence or invoking circuitry for enabling, triggering, coordinating, requesting, or otherwise causing one or more occurrences of virtually any functional operations described herein. In some variants, operational or other logical descriptions herein may be expressed as source code and compiled or otherwise invoked as an executable instruction sequence. In some contexts, for example, implementations may be provided, in whole or in part, by source code, such as C++, or other code sequences. In other implementations, source or other code implementation, using commercially available and/or techniques in the art, may be compiled
  • a high-level descriptor language e.g., initially implementing described technologies in C or C++ programming language and thereafter converting the programming language implementation into a logic-synthesizable language implementation, a hardware description language implementation, a hardware design simulation implementation, and/or other such similar mode(s) of expression).
  • a logical expression e.g., computer programming language implementation
  • a Verilog-type hardware description e.g., via Hardware Description Language (HDL) and/or Very High Speed Integrated Circuit Hardware Descriptor Language (VHDL)
  • VHDL Very High Speed Integrated Circuit Hardware Descriptor Language
  • Those skilled in the art will recognize how to obtain, configure, and optimize suitable transmission or computational elements, material supplies, actuators, or other structures in light of these teachings.
  • examples of such other devices and/or processes and/'or systems might include - as appropriate to context and application ⁇ all or part of devices and/or processes and/or systems of (a) an air conveyance (e.g., an airplane, rocket, helicopter, etc.) , (b) a ground conveyance (e.g., a car, truck, locomotive, tank, armored personnel carrier, etc.), (c) a building (e.g., a home, warehouse, office, etc.), (d) an appliance (e.g., a refrigerator, a washing machine, a dryer, etc.), (e) a communications system (e.g., a networked system, a telephone system, a Voice over IP system, etc.), (f a business entity (e.g., an Internet Sendee Provider (ISP) entity such as Comcast Cable, Qwest, Southwestern Bell, etc.), or (g) a wired/wireless services entity (e.g.. Sprint, C
  • ISP Internet Sendee Provide
  • use of a system or method may occur in a territory even if components are located outside the territory.
  • use of a distributed computing system may occur in a. territory even though parts of the system may be located outside of the territory (e.g., relay, server, processor, signal- bearing medium, transmitting computer, receiving computer, etc. located outside the territory).
  • a sale of a system or method may likewise occur in a territory even if components of the system or method are located and/or used outside the territory.
  • implementation of at least part of a system for performing a method in one territory does not preclude use of the system in another territory
  • any two components so associated can also be viewed as being “operably connected”, or “operably coupled,” to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable,” to each other to achieve the desired functionality.
  • operably couplable include but are not limited to physically mateable and/or physically interacting components, and/or wirelessly interactable, and/or wirelessly interacting components, and/or logically interacting, and/or logically interactable components.
  • one or more components may be referred to herein as “configured to,” “configured by,” “configurable to,” “operable/operative to,”
  • electro-mechanical system includes, but is not limited to, electrical circuitry operably coupled with a transducer (e.g., an actuator, a motor, a piezoelectric crystal, a Micro Electro Mechanical System (MEMS), etc.), electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitiy forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a.
  • a transducer e.g., an actuator, a motor, a piezoelectric crystal, a Micro Electro Mechanical System (MEMS), etc.
  • MEMS Micro Electro Mechanical System
  • electrical circuitry forming a memory device e.g., forms of memory (e.g., random access, flash, read only, etc,)
  • electrical circuitry forming a communications device e.g., a modem
  • electro-mechanical systems include but are not limited to a variety of consumer electronics systems, medical devices, as well as other systems such as motorized transport systems, factory automation systems, security systems, and/or communication/computing systems.
  • electro-mechanical as used herein is not necessarily limited to a system that has both electrical and mechanical actuation except as context may dictate otherwise.
  • electrical circuitry includes, but is not limited to, electrical circuitry having at least one discrete electrical circuit, electrical circuitry having at least one integrated circuit, electrical circuitry having at least one application specific integrated circuit, electrical circuitry forming a general purpose computing device configured by a computer program (e.g., a general purpose computer configured by a computer program which at least partially carries out processes and/or devices described herein, or a microprocessor configured by a computer program which at least partially carries out processes and/or devices described herein), electrical circuitiy forming a memoiy device (e.g., forms of memory (e.g., random access, flash, read only, etc.)), and/or electrical circuitry forming a communications
  • a typical image processing system generally includes one or more of a system unit housing, a video display device, memory such as volatile or non-volatile memory, processors such as microprocessors or digital signal processors, computational entities such as operating systems, drivers, applications programs, one or more interaction devices (e.g., a touch pad, a touch screen, an antenna, etc.), control systems including feedback loops and control motors (e.g., feedback for sensing lens position and/or velocity; control motors for moving/distorting lenses to give desired focuses).
  • An image processing system may be implemented utilizing suitable commercially available components, such as those typically found in digital still systems and/or digital motion systems.
  • a data processing system generally includes one or more of a system unit housing, a video display device, memory such as volatile or nonvolatile memory, processors such as microprocessors or digital signal processors, computational entities such as operating systems, drivers, graphical user interfaces, and applications programs, one or more interaction devices (e.g., a touch pad, a touch screen, an antenna, etc.), and/or control systems including feedback loops and control motors (e.g., feedback for sensing position and/or velocity; control motors for moving and/or adjusting components and/or quantities).
  • a data processing system may be implemented utilizing suitable commercially available components, such as those typically found in data computing/communication and/or network computing/communication systems.
  • cloud computing may be understood as described in the cloud computing literature.
  • cloud computing may be methods and/or systems for the delivery of computational capacity and-'or storage capacity as a service.
  • the "cloud” may refer to one or more hardware and/or software components that deliver or assist in the delivery of computational and/or storage capacity, including, but not limited to, one or more of a client, an application, a platform, an infrastructure, and/or a server
  • the cloud may refer to any of the hardware and/or software associated with a client, an application, a platform, an infrastructure, and/or a server.
  • cloud and cloud computing may refer to one or more of a computer, a processor, a storage medium, a router, a switch, a.
  • a cloud may refer to a private cloud, a public cloud, a hybrid cloud, and/or a community cloud.
  • a cloud may be a shared pool of configurable computing resources, which may be public, private, semi- private, distributable, scaleahle, flexible, temporary, virtual, and/or physical.
  • a cloud or cloud service may be delivered over one or more types of network, e.g., a mobile communication network, and the Internet.
  • a cloud or a cloud service may include one or more of mrrastructure-as-a-service (“laaS”), platform-as-a-service (“PaaS”), software-as-a- service (“SaaS”), and/or desktop-as-a-service (“DaaS”).
  • laaS mrrastructure-as-a-service
  • PaaS platform-as-a-service
  • SaaS software-as-a-service
  • DaaS desktop-as-a-service
  • laaS may include, e.g., one or more virtual server instantiations that may start, stop, access, and/or configure virtual servers and/or storage centers (e.g., providing one or more processors, storage space, and'or network resources on-demand, e.g., EMC and Rackspace),
  • PaaS may include, e.g., one or more software and/or development tools hosted on an infrastructure (e.g., a computing platform and/or a solution stack from which the client can create software interfaces and applications, e.g., Microsoft Azure).
  • SaaS may include, e.g., software hosted by a service provider and accessible over a network (e.g., the software for the application and/or the data associated with that software application may be kept on the network, e.g., Google Apps, SalesForce).
  • DaaS may include, e.g., providing desktop, applications, data, and/or services for the user over a network (e.g., providing a multi-application framework, the applications in the framework, the data associated with the applications, and/or services related to the applications and/or the data over the network, e.g., Citrix).
  • the foregoing is intended to be exemplary of the types of systems and/or methods referred to in this application as "cloud” or “cloud computing” and should not be considered complete or exhaustive.
  • ATMs Automated Teller Machines
  • Airline ticket counter machines check passengers in, dispense tickets, and allow passengers to change or upgrade flights.
  • Train and subway ticket counter machines allow passengers to purchase a ticket to a particular destination without invoking a human interaction at all.
  • Many groceries and pharmacies have self-service checkout machines which allow a consumer to pay for goods purchased by interacting only with a machine.
  • smartphones and tablet devices also now are configured to receive speech commands.
  • Speech and voice controlled automobile systems now appear regularly in motor vehicles, even in economical, mass-produced vehicles.
  • Home entertainment devices e.g., disc players, televisions, radios, stereos, and the like, may respond to speech commands.
  • home security systems may respond to speech commands.
  • a worker's computer may respond to speech from that worker, allowing faster, more efficient work flows.
  • Such systems and machines may he trained to operate with particular users, either through explicit training or through repeated interactions. Nevertheless, when that system is upgraded or replaced, e.g., a new television is purchased, that training may be lost with the device.
  • adaptation data for speech recognition systems may be separated from the device which recognizes the speech, and may be more closely associated with a user, e.g., through a. device carried by the user, or through a network location associated with the user.
  • FIG. 1 e.g., Fig. 1A
  • Fig. 1 A illustrates an example
  • personal de vice 20A personal de vice 20B
  • intermediate device 40 target device 3 OA
  • target device 30B target device 30B
  • personal device 520 which in some embodiments, may be an example of one of persona! device 20A, persona! device 20B, and intermediate device 40.
  • device 120 in various embodiments, may be endowed with logic that is designed for managing adaptation data, wherein the adaptation data is correlated to at least one aspect of speech of a particular party, logic that is designed for facilitating transmission of the adaptation data to a target device, in response to an indicator related to a speech-facilitated transaction of a particular party, wherein the adaptation data is correlated to at l east one aspect of speech of the particular party, and logic that is designed for determining whether to update the adaptation data, said determination at least partly based on a result of at lea st a portion of the speech- facilitated transaction.
  • a user 105 may engage in a speech facilitated transaction with one or more of a terminal device 30A and a terminal device SOB.
  • the speech-facilitated transaction may be directed to one of terminal device 30A or terminal device SOB.
  • the user may not specifically direct her speech toward terminal device 30A or terminal device SOB, but rather to both of them, with indifference toward which device carries out the speech-facilitated transaction.
  • one of the terminal device 30A and terminal device SOB negotiate between themselves to determine which device will carry out the speech-facilitated transaction.
  • one or more of the personal device 20A, the personal device 20B, and the intermediate device 40 may determine which of the terminal device 3 OA and terminal device 30B carries out the speech-facilitated transaction. In some embodiments, one or more of personal device 20A, personal device 20B, and intermediate device 40 may detect, one or more of terminal device 30A and terminal device SOB, establish a connection, or negotiate with one or more of terminal devices 30A and 30B.
  • the dashed-line arrows shown in environment 100 of Fig. 1 A are not, labeled, but are intended to show the flow of data from one device to the other. Some data connections are omitted for simplicity of drawing, e.g., although there is no arrow, personal device 20A may communicate directly with terminal device 30A and terminal device SOB.
  • the flow of data may include one or more adaptation data, speech data in any format, including raw speech from the user, adaptation result data, intended target data, target data, and the like.
  • the dotted line arrows show an association between the user 105 and one or more of personal device 20A, personal device 20B, and intermediate device 40.
  • any or all of personal devices 20A, 20B, and 40 may communicate with any or all of terminal device 30A and terminal device 30B, either directly, or indirectly.
  • these devices communicate with each other via a server 1 10, which may be local or remote to any of the devices 20A, 20B, 30A, 30B, and 40.
  • these devices communicate with each other via one or more communication networks 140, which may be local or remote to any of the devices 20A, 20B, 30A, SOB, and 40.
  • server 1 10 and communication network 40 are pictured in each of the embodiments in Figs. 1A and 1C-1E, server 110 and communication network 140 are not required, and are shown merely for purposes of illustration.
  • Fig. 1 A shows personal device 20A, personal device 20B, intermediate device 40, terminal device 30A, terminal device 3GB, and server 110.
  • the number of devices is shown merely for illustrative purposes. In some embodiments, however, there may be a different number of personal devices, intermediate devices, terminal devices, servers, and communication networks. In some embodiments, one or more of the personal devices, intermediate devices, terminal devices, servers, and communication networks may be omitted entirely.
  • personal device 20A and 20B are shown as associated with user 105.
  • This association may be attenuated, e.g., they may merely be in the same physical proximity. In other embodiments, the association may be one of ownership, mutual contract, information storing, previous usage, or other factors.
  • the examples described further herein will provide a non-exhaustive list of examples of relationships between user 105 and a personal device 120 (e.g., personal device 20A or personal device 20B).
  • personal device 120 e.g., personal device 20A or personal device 20B
  • personal device 120 may be any size and have any specification.
  • Personal device 120 may be a custom device of any shape or size, configured to transmit, receive, and store data.
  • Personal device 520 e.g., personal device 20A or personal device 20B
  • personal device 120 may be a modified object that is worn, e.g., eyeglasses, a wallet, a credit card, a watch, a chain, or an article of clothing. Anything that is configured to store, transmit, and receive data may be a personal device 120 (e.g., personal device 20A or personal device 20B), and personal device 120 (e.g., personal device 20A or personal device 20B) is not limited in size to devices that are capable of being carried by a user.
  • personal device 120 may not be in direct proximity to the user, e.g., personal device 120 (e.g., personal device 20A or personal device 20B) may be a computer sitting on a desk in a user's home or office.
  • terminal device 30* terminal device 30A and SOB (hereinafter interchangeably collectively or individually referred to as “terminal device 30*”) are described as “terminal device,” this is merely for simplicity of illustration, terminal device 30* could be any device that is configured to receive speech.
  • terminal device 30* may be a terminal, a computer, a navigation system, a phone, a. piece of home electronics (e.g., a DVD player, Blu-Ray player, media player, game system, television, receiver, alarm clock, and the like).
  • Terminal device 30* may, in some embodiments, be a home security system, a safe lock, a door lock, a kitchen appliance configured to receive speech, and the like.
  • terminal device 30* may be a motorized vehicle, e.g., a car, boat, airplane, motorcycle, golf cart, wheelchair, and the like.
  • terminal device 30* may be a piece of portable electronics, e.g., a laptop computer, a netbook computer, a tablet device, a smartphone, a cellular phone, a radio, a portable navigation system, or any other piece of electronics capable of receiving speech.
  • Terminal device 30* may be a part of an enterprise solution, e.g., a common workstation in an office, a copier, a scanner, a personal workstation in a cubicle, an office directory, an interactive screen, and a telephone.
  • target device 30A and/or target device 30B receives adaptation data from the personal device 120 (e.g., personal device 2 OA. or personal device 20B), in a process that will be described in more detail herein.
  • the adaptation data is transmitted over one or more communication network(s) 40.
  • the communication network 40 may include one or more of a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a wireless local area network (WLAN), a personal area network (PAN), a Worldwide Interoperability for Microwave Access (WiMAX), public switched telephone network (PTSN), a general packet radio service (GPRS) network, a cellular network, and so forth.
  • the communication networks 40 may be wired, wireless, or a. combination of wired and wireless networks. It is noted that "communication network” here refers to one or more communication networks, which may or may not interact with each other.
  • the adaptation data does not come directly from the personal device 120 (e.g., personal device 20A or personal device 20B).
  • personal device 120 e.g., personal device 20A or personal device 20B
  • personal device 120 merely facilitates communication of the adaptation data, e.g., by providing one or more of an address, credentials, instructions, authorization, and recommendations.
  • personal device 120 e.g., personal device 20A or personal device 20B
  • personal device 120 retrieves adaptation data from server 10 upon a request from the terminal device 30, and then relays or facilitates in the relaying of the adaptation data to terminal device 30.
  • personal device 120 (e.g., personal device 20A or personal device 20B) broadcasts the adaptation data regardless of whether a terminal device 30 is listening, e.g., at predetermined, regular, or otherwise-defined intervals.
  • personal device 120 e.g., personal device 2 OA. or personal device 20B
  • user 5 determines when personal device 120 (e.g., personal device 20A or personal device 20B) broadcasts adaptation data.
  • a third party triggers the transmission of adaptation data to the terminal device 30, in which the transmission is facilitated by the personal device 120 (e.g., personal device 20A or personal device 20B).
  • the personal device 120 e.g., personal device 20A or personal device 20B.
  • Fig. IB shows a more detailed description of a personal device 120 in an exemplary environment 100.
  • Personal device 120 may be an example of personal device 20A or 20B of Fig. 1A, intermediate device 40 of Fig. 1A, first personal device 21 A of Fig. IC, second personal device 21B of Fig, ID, one of the modules of device 31 of Fig. IC, personal device 22A of Fig. ID, personal device 22B of Fig. ID, any of devices 51, 52, 53, and 54 of Fig.
  • the personal device 120 may comprise, among other elements, a processor 132, a memory 134, a user interface 135, a speech detection interface 138, and a data transmission interface 137. Each of these elements may be absent in various embodiments of personal device 120, e.g., some personal devices 120 may not have a speech detection interface 138, or a memory 134, or a user interface 135.
  • Processor 132 may include one or more microprocessors. Central Processing Units (“CPU”), a Graphics Processing Units ("GPU"), Physics Processing Units, Digital Signal Processors, Network Processors, Floating Point Processors, and the like. In some embodiments, processor 132 may be a server. In some embodiments, processor 132 may be a distributed-core processor. Although processor 132 is as a single processor that is part of a single personal device 120, processor 132 may be multiple processors distributed over one or many computing devices 30, which may or may not be configured to operate together. Processor 132 is illustrated as being configured to execute computer readable instructions in order to execute one or more operations described above, and as illustrated in Figs.
  • processor 132 is designed to be configured to operate as processing module 550, which may include one or more of speech adaptation data related to at least one aspect of a particular party regulating module 1 2, speech adaptation data transmission, in response to indication of a speech- facilitated transaction including a target device, facilitating module 154, and speech adaptation data potential modification determining partly based on result of portion of speech- facilitated transaction module 156.
  • memory 134 may comprise of one or more of one or more mass storage devices, read-only memory (ROM), programmable read-only memoiy (PROM), erasable programmable read-only memory (EPROM), cache memory such as random access memoiy (RAM), flash memoiy, synchronous random access memoiy (SRAM), dynamic random access memory (DRAM), and/or other types of memory devices.
  • ROM read-only memory
  • PROM programmable read-only memoiy
  • EPROM erasable programmable read-only memory
  • cache memory such as random access memoiy (RAM), flash memoiy, synchronous random access memoiy (SRAM), dynamic random access memory (DRAM), and/or other types of memory devices.
  • memoiy 134 may be located at a single network site. In some embodiments, memoiy 134 may be located at multiple network sites, including sites that are distant from each other.
  • personal device 120 may include a user interface 135.
  • the user interface may be implemented in hardware or software, or both, and may include various input and output devices to allow an operator of personal device 120 to interact with personal device 120.
  • user interface 135 may include, but is not limited to, an audio display, e.g., a speaker 108, a video display, e.g., a screen 102, a microphone, a camera, a keyboard, e.g., keyboard 103, a trackball, e.g., trackball 104, a mouse, e.g., mouse 105, one or more soft keys, e.g., hard/soft keys 106, a touch input, e.g., touchscreen 107, e.g., which may also be a video display screen, a joystick, a game controller, a touchpad, a handset, or any other device that allows interaction between a device and a user.
  • an audio display e.g., a speaker 108
  • a video display e.g., a screen 102
  • a microphone e.g., a camera
  • a keyboard e.g., keyboard 103
  • a trackball e.g
  • a speech detection interface 138 may be configured to receive and/or process speech as input, or to observe and/or record speech of a speech- facilitated transaction
  • a speech detection interface 138 may include a speech indicator receiver 1 12, which may be a sensor of any type, or a communication port that, receives a signal, or a sensor that detects a button press, or any other module that can detect a change of state of any kind in the environment 100, whether internal or external to the device.
  • the speech detection interface 138 may, in some embodiments, include a microphone 110, which may or may not communicate with speech indicator receiver 112. In some embodiments, microphone 110 may detect speech, either selectively or always-on, and may be controlled by one or more of speech indicator receiver 1 12 and processor 132.
  • Data transmission interface 137 may, in some embodiments, handle the transmission and reception of data by the device.
  • data transmission interface 137 may include an adaptation data transmitter/receiver 114, which handles the reception and transmission of adaptation data over any type of network or internal form of communication, e.g., internal bus, and the like.
  • Data, transmission interface 137 may, in some embodiments, include speech data transmitter/receiver 116, which may handle the reception and transmission of speech data., including raw speech, over any form of moving data.
  • personal device 120 may have one or more sensors 182, These sensors include, but are not limited to, a Global Positioning System (GPS) sensor, a still camera, a video camera, an altimeter, an air quality sensor, a. barometer, an accelerometer, a charge-coupled device, a radio, a thermometer, a pedometer, a heart monitor, a moisture sensor, a humidity sensor, a microphone, a seismometer, and a magnetic field sensor. Sensors 182 may interface with sensor interface 180. Although Fig. IB illustrates sensors 182 as pari, of personal device 120, in some embodiments, sensors 182 may be separated from personal device 120, and communicate via one or more communication networks, e.g., communication networks 140.
  • GPS Global Positioning System
  • FIG. 1 C shows an example embodiment of an exemplary environment 00', which is a non-limiting example of an environment 100.
  • environment, 100' may include a user (not shown), which user may have one or more of a first personal device 21.
  • First persona! device 21 A may be, for example, a USB drive
  • second personal device 21B may be, for example, a cellular telephone device, although both personal device 21 A and personal device 21B may be any form of personal device 120 as previously described.
  • first personal device 21 A and second personal device 2 IB may interact with device 31, which may be any type of computing device, e.g., laptop computer, desktop computer, server, netbook, tablet device, smartphone, and the like.
  • Device 31 may have an operating system software 81 loaded thereon.
  • Operating system software 81 may include, but is not limited to, Microsoft Windows, Google Android, Apple iOS, Apple Mountain Lion, UNIX, Linux, Chrome OS, Symbian, and the like.
  • device 31 may include an enterprise client software 82 onboard.
  • client software e.g., Citrix, or the like
  • this module may play a role in determining the role of the interpretation of speech data (e.g., speech data 101) and the application of adaptation data.
  • device 31 also may include one or more of first application software 91 and second application software 92.
  • First, and second application software 91 and 92 may be any type of application, e.g., game, spreadsheet, word processor, web browser, chat client, picture viewer, picture manipulator, webcam application, and the like.
  • these modules may play a role in determining the role of the interpretation of speech data and the application of adaptation data.
  • the complexity of the application may play a rol e in determining how much of the speech processing occurs at the appl ication level .
  • device 35 may communicate with one or more communication networks 140 and one or more servers 1 10.
  • Fig. ID shows an example embodiment of an exemplary environment 100", which is a non-limiting example of an environment 100.
  • environment 100' ' may include a user 105, which user may have one or more of a personal device 22 A and a personal device 22B.
  • Personal device 22A may be, for example, a universal remote control
  • personal device 22B may be, for example, a cellular telephone device, although both personal device 22A and personal device 22B may be any form of personal device 120 as previously described.
  • one or both of personal device 22A, personal device 22B, and computing device 54 may transmit, store, and/or receive adaptation data.
  • one of personal device 22A, personal device 22B, and computing device 54 may determine to which of the devices shown in Fig. ID the user 105 is directing her speech.
  • one or more of receiver device 51 , media player device 52, and television device 53 may transmit one or more of speech data and adaptation data back and forth, and one or more of receiver device 51, media player device 52, and television device 53 may determine which device should apply the adaptation data., and which device should process the speech data, out of devices 22A, 22B, 51, 52, 53, and 54.
  • Fig. IE shows an example embodiment of an exemplary environment 100"', which is a non-limiting example of an environment 100.
  • environment 100"' may include a user (not shown) driving an automobile (interior only shown), wherein the automobile is equipped with a motor vehicle control system 42, which may control the non-driving features of the automobile, e.g., music, climate, temperature, fuel management, seat position, media playing, lights, and the like.
  • the automobile also may have a smart key device 26, which, in some embodiments, may store, receive, and/or transmit adaptation data, either wirelessly or through the system, of the automobile.
  • environment 100' " may also include a GPS navigation device 41 , which may be an example of intermediate de vice 40, which also may be a personal device 120.
  • GPS navigation device 41 may serve as a terminal device, receiving speech data and adaptation data in order to process a user's request.
  • GPS navigation device 41 may serve as a personal device 120, storing adaptation data derived from navigation commands of the user, and transmitting the adaptation data to a target device, e.g., motor vehicle control system 42, when needed.
  • Intermediate devices 40 e.g., as shown in Fig. I A
  • GPS navigation device 41 which may be an example of intermediate device 40, may be a personal device 120 for a first transaction and a terminal device in a second transaction.
  • GPS navigation device 41 may change its role based on an analysis of data received by GPS navigation device 41.
  • GPS navigation device 41 , motor vehicle control system 42, smart key device 26, and the user's personal device may communicate with one or more communication networks 140 and one or more servers 1 10. As in all shown exemplary embodiments, however, these elements are optional and some embodiments may exclude them.
  • Fig. 2 illustrates an exemplary implementation of the speech adaptation data related to at least one aspect of a particular party regulating module 152.
  • the speech adaptation data related to at least one aspect of a particular party regulating module 152 may include one or more sub-logic modules in various alternative implementations and embodiments. For example, as shown in Fig. 2, e.g., Fig.
  • module 152 may include one or more of speech adaptation data, related to at least one prior interaction between a particular party and a device regulating module 202, address at which speech adaptation data is stored obtaining module 204, determining, at a particular time, whether the speech adaptation data is available at the obtained address module 206, speech adaptation data related to at least one aspect of a particular party obtaining module 208, speech adaptation data related to at least one aspect of a particular party access monitoring module 210, speech adaptation data linking pronunciation of one or more syllables to one or more concepts regulating module 212, address of adaptation data generating module 214, and address of adaptation data pro viding module 216.
  • module 152 ma - include one or more of address of adaptation data generating module 218 and address of adaptation data providing upon request module 220.
  • Fig. 3 illustrates an exemplary implementation of speech adaptation data transmission, in response to indication of a speech-facilitated transaction including a target device, facilitating module 154.
  • the speech adaptation data transmission in response to indication of a speech-facilitated transaction including a target device, facilitating module 154 may include one or more sub-logic modules in various alternative implementations and embodiments. For example, as shown in Fig. 3 (e.g., Fig.
  • module 154 may include one or more of speech adaptation data including a list of one or more words and corresponding pronunciations transmission, in response to indication of a speech- facilitated transaction including a target device, facilitating module 302, speech adaptation data including instructions for modifying a word frequency table transmission, in response to indication of a speech-facilitated transaction including a. target device, facilitating module 304, speech adaptation data including phrase completion algorithm transmission, in response to indication of a speech-facilitated transaction including a target device, facilitating module 306, and speech adaptation data transmission, in response to indication of intention to conduct a speech-facilitated transaction, facilitating module 308.
  • module 308 may include speech adaptation data transmission, in response to receipt from target device of indication of intention to conduct a speech-facilitated transaction, facilitating module 310.
  • module 310 may include one or more of speech adaptation data transmission, in response to receipt from target device of indication that the particular party interacted with an input portion of the target device, facilitating module 312, speech adaptation data transmission, in response to receipt indication that the particular party is directing speech to the target device, facilitating module 314, and speech adaptation data transmission, in response to request for speech adaptation data, facilitating module 356.
  • module 154 may include module 308, as described above.
  • module 308 may include speech adaptation data transmission, in response to received indication of intention to conduct a speech- facilitated transaction from a particular device, facilitating module 31 8.
  • module 154 may include one or more of first speech adaptation data rel ated to first aspect of speech of particular party
  • module 320 may include one or more of first speech adaptation data related to a manner of speaking of particular party transmitting to target device module 324 and first speech adaptation data related to expected vocabulary transmitting to target device module 326.
  • module 326 may include first speech adaptation data related to vocabulary estimated to be used in course of speech-facilitated transaction to target device module 328
  • module 328 may include one or more of first speech adaptation data related to one or more words related to food items estimated to be used in course of speech- facilitated transaction to target device module 330 and first speech adaptation data related to one or more words related to one or more electronics estimated to be used in course of speech- facilitated transaction to target device module 332
  • module 322 may include second speech adaptation data related to second aspect of speech of particular party transmitting in response to target device request module 340.
  • module 154 may include one or more of module 320 and module 322, as described above.
  • module 320 may include one or more of first speech adaptation data, related to a spoken language transmitting to target device module 334 and first speech adaptation data related to previous speech-facilitated transactions by members of a group having a particular trait transmitting to target device module 336
  • module 336 may include first speech adaptation data related to previous speech-facilitated transactions by members of a group from a particular community transmitting to target, device module 338
  • module 322 may include one or more of second speech adaptation data related to second aspect that is different than the first aspect transmitting to target device module 342.
  • module 342 may include second speech adaptation data related to previous speech interactions of second group that is different than a first group related to the first speech adaptation data transmitting to target device module 344.
  • module 344 may include second speech adaptation data related to previous speech interactions of second group that is smaller than a first group related to the first speech adaptation data transmitting to target device module 346.
  • Fig. 4 illustrates an exemplary implementation of the speech adaptation data potential modification determining partly based on result of portion of speech-facilitated transaction module 556.
  • the speech adaptation data potential modification determining partly based on result of portion of speech- facilitated transaction module 156 may include one or more sub-logic modules in various alternative implementations and embodiments. For example, as shown in Fig, 4, e.g., Fig.
  • module 156 may include one or more of speech adaptation data modification partly based on result of portion of speech- facilitated transaction module 402, speech adaptation data determining against modification partly based on result of portion of speech-facilitated transaction module 404, speech adaptation data potential modification determining partly based on result of finished speech- facilitated transaction module 406, speech adaptation data potential modification determining partly based on result of an initial portion of the speech- facilitated transaction module 408, speech adaptation data potential modification determining partly based on a first sentence of the speech facilitated transaction occurring prior to a second sentence of the speech-facilitated transaction module 410, speech adaptation data potential modification determining partly based on result of portion of speech -facilitated transaction and made before the speech- facilitated transaction ends module 412, and speech adaptation data potential modification determining partly based on result of a portion of speech- facilitated transaction after at least one word is spoken by the particular party module 414.
  • module 414 may include speech adaptation data potential modification determining partly based on result of portion of speech- facilitated transaction and made prior to a particular
  • module 156 may include one or more of portion of speech-facilitated transaction result acquiring module 418 and speech adaptation data potential modification determining partly based on acquired result module 420.
  • module 458 may include one or more of portion of speech-facilitated transaction result acquiring from target device module 422, portion of speech-facilitated transaction result acquiring from particular party module 424, and portion of speech-facilitated transaction result indicating less than complete success of the transaction acquiring module 432 (e.g., which, in some embodiments, may include portion of speech-facilitated transaction result indicating incomplete or low confidence level interpretation of portion of transaction acquiring module 434.
  • module 458 may include one or more of portion of speech-facilitated transaction result acquiring from target device module 422, portion of speech-facilitated transaction result acquiring from particular party module 424, and portion of speech-facilitated transaction result indicating less than complete success of the transaction acquiring module 432 (e.g., which, in some embodiments, may include portion of speech-facilitated transaction result indicating incomplete or low confidence level interpretation of portion of transaction acquiring module 434.
  • module 424 may include one or more of portion of speech-facilitated transaction result acquiring from particular device operated by particular party module 426 (e.g., which, in some embodiments, may include portion of speech- facilitated transaction result acquiring at least partially as a result of a. pushed button of the particular device module 428) and portion of speech-facilitated transaction feedback acquired from particular device operated by particular party module 430.
  • particular party module 426 e.g., which, in some embodiments, may include portion of speech- facilitated transaction result acquiring at least partially as a result of a. pushed button of the particular device module 428, and portion of speech-facilitated transaction feedback acquired from particular device operated by particular party module 430.
  • module 156 may include one or more of module 418 and module 420, as previously described.
  • module 418 may include one or more of portion of speech-facilitated transaction result indicating failure of the target device to carry out at least a portion of the speech- facilitated transaction acquiring module 436, portion of speech- facilitated transaction result acquiring from particular party via target device module 442, portion of speech-facilitated transaction result including one or more interpreted words acquiring module 444, portion of speech-facilitated transaction result including one or more words not completely interpreted acquiring module 446, and portion of speech- facilitated transaction result including a representation of the particular party speaking one or more words of the speech-facilitated transaction acquiring module 448.
  • module 436 may include one or more of portion of speech-facilitated transaction result indicating target-device determined error rate is above a threshold value acquiring module 438 and portion of speech- facilitated transaction result indicating target-device determined confidence rate is below a threshold value acquiring module 440.
  • module 156 may include one or more of module 418 and module 420, as previously described .
  • module 418 may include one or more of portion of speech-facilitated transaction result including a representation of the target device interpreting one or more words of the speech- facilitated transaction acquiring module 450 and portion of speech-facilitated transaction result including a data structure configured to store one or more interpreted words of the speech-facilitated transaction and one or more corresponding representations of pronunciations of the one or more interpreted words acquiring module 452.
  • module 156 may include one or more of portion of speech- facilitated transaction at least a portion of result receiving module 454, speech adaptation data modifying at least partly based on received at least a portion of the result module 456, updated speech adaptation data transmitting before completion of speech-facilitated transaction module 458, portion of speech-facilitated transaction result receiving module 460, speech adaptation data modifying partly based on result module 462, portion of speech-facilitated transaction at least a portion of result obtaining module 464, speech adaptation data adapting at least partly based on received at least a portion of the result module 466, and signal indicating adaptation of a speech adaptation data transmitting module 468.
  • module 156 may include one or more of potential modification data receiving from further device module 470, applying the potential modification data received from the further device to the speech adaptation data module 472, speech-facilitated transaction result portion receiving module 474, speech- facilitated transaction result portion transmitting to device module 476 (e.g., which, in some embodiments, may include speech-facilitated transaction result portion transmitting to device that stores speech adaptation data module 480), determination data indicating whether to modify the speech adaptation data receiving from, device module 478 (e.g., which, in some embodiments, may include determination data instructing how to modify the speech adaptation data receiving from device module 482), speech-facilitated transaction result portion reception module 484, speech adaptation data modification decision making partly based on speech-facilitated transaction result portion module 486, request to modify speech adaptation data transmitting to device module 488, and modified adaptation data acquiring from device module 490.
  • device module 478 e.g., which, in some embodiments, may include determination data instructing how to modify the speech adaptation data receiving from device module 482
  • speech-facilitated transaction result portion reception module 484
  • module 156 may include one or more of result of particular portion of speech-facilitated transaction acquiring module 492, speech adaptation data potential modification application at least partly based on acquired result of the particular portion of speech-facilitated transaction module 494, and modified speech adaptation data configured to be applied by the target device to a further portion of the speech- facilitated transaction transmitting module 496.
  • module 494 may include algorithm of speech adaptation data potential modification apply at least partly based on acquired result of the particular portion of speech-facilitated transaction module 498.
  • module 498 may include one or more of parameter of algorithm of speech adaptation data potential modification application at least partly based on acquired result of the particular portion of speech-facilitated transaction module 401 and different algorithm of speech adaptation data selecting at least partly based on acquired result of the particular portion of speech- facilitated transaction module 403.
  • module 156 may include one or more of module 492, module 494, and module 496, as previously described. In some
  • module 494 may include pronunciation dictionary potential modification application at least partly based on acquired result of the particular portion of speech- facilitated transaction module 405.
  • module 405 may include one or more of one or more word adding to pronunciation dictionary at least partly based on acquired result of the particular portion of speech- facilitated transaction module 407, one or more word adding to pronunciation dictionary at least partly based on acquired result of the particular portion of speech-facilitated transaction that includes the one or more words module 409, and pronunciation dictionary substitution at least, partly based on acquired result of the particular portion of speech-facilitated transaction module 41 1 .
  • module 156 may include one or more of module 492, module 494, and module 496, as previously described. In some
  • module 494 may include path of path selection algorithm potential modification application at least partly based on acquired result of the particular portion of speech-facilitated transaction module 413.
  • module 496 may include one or more of modified speech adaptation data configured to be used to interpret a further portion of the speech-facilitated transaction transmitting module 415, modified speech adaptation data configured to be used to interpret a further portion of the speech- facilitated transaction directly after interpreting the particular portion transmitting module 417, modified speech adaptation data transmitting module 419, and indication of receipt of modified speech adaptation data recei ving module 421.
  • module 156 may include one or more of module 492, module 494, and module 496, as previously described, in some
  • module 496 may include one or more of modified speech adaptation data transmitting module 423, indication of application of modified speech adaptation data receiving module 425, and indication of readiness to complete further portion of speech- facilitated transaction presentation facilitating module 427.
  • module 427 may include one or more of message indicating readiness of further portion of speech-facilitated transaction displaying module 429 and message indicating readiness of further portion of speech- facilitated transaction display on target device facilitating module 431.
  • FIGs. 2-4 various operations may be depicted in a box-within-a-box manner. Such depictions may indicate that an operation in an internal box may comprise an optional example embodiment of the operational step illustrated in one or more external boxes. However, it should be understood that internal box operations may be viewed as independent operations separate from any associated external boxes and may be performed in any sequence with respect to all other illustrated operations, or may be performed concurrently. Still further, these operations illustrated in Figs. 5-8 as well as the other operations to be described herein may be performed by at least one of a machine, an article of manufacture, or a composition of matter.
  • Fig. 5 shows operation 500, which may include operation 502 depicting managing adaptation data, wherein the adaptation data is correlated to at least one aspect of speech of a particular party.
  • Fig. 1 e.g., Fig.
  • IB shows speech adaptation data related to at least one aspect of a particular party regulating module 152 managing (e.g., storing, tracking, monitoring, authorizing, changing the permissions of, providing access, allocating storage for, retrieving, receiving, processing, altering, comparing, or otherwise performing one or more operations on adaptation data), wherein the adaptation data (e.g., a phrase completion algorithm used to assist in interpreting spoken words based on context) is correlated to at least one aspect of speech of a particular party (e.g., the user previously conducted a speech-facilitated transaction with a different automated teller machine device terminal, and the groupings of words spoken by the user assisted in forming the contextual rules used by at least a portion of the phrase completion algorithm).
  • the adaptation data e.g., a phrase completion algorithm used to assist in interpreting spoken words based on context
  • operation 500 may include operation 504 depicting facilitating transmission of the adaptation data to a target device, in response to an indicator related to a speech-facilitated transaction of a particular party, wherein the adaptation data is correlated to at least one aspect of speech of the particular party.
  • Fig. 1 e.g., Fig.
  • IB shows speech adaptation data transmission, in response to indication of a speech-facilitated transaction including a target device, facilitating module 154 facilitating transmission (e.g., transmitting, or taking one or more steps that will assist in the transmission of, regardless of the starting or ending point) of the adaptation data (e.g., a phrase completion algorithm used to assist in interpreting spoken words based on context) to a target device, in response to an indicator (e.g., a user either stands in front of an automated teller machine device terminal, or inserts her card, or pushes a button, or speaks a start phrase that is displayed on the screen of the automated teller machine device terminal) of a speech-facilitated transaction (e.g., withdrawing two hundred dollars from the automated teller machine device terminal by commanding the automated teller machine device using speech commands for at least part of the transaction) of a particular party (e.g., the user of the automated teller machine device), wherein the adaptation data is correlated to at least one aspect of speech of the particular part ⁇ ' (e.g.,
  • operation 500 may include operation 506 depicting determining whether to update the adaptation data, said determination at least partly based on a result of at least a portion of the speech-facilitated transaction.
  • Fig. 1 e.g., Fig.
  • IB shows speech adaptation data potential modification determining partly based on result of portion of speech-facilitated transaction module 156 determining whether to update (e.g., change, modify, change a timestamp of, change a parameter of, delete, supplement, alter, transmit to a different location, change an address of, refresh, or other operation) the adaptation data (e.g., the phrase completion algorithm used to assist in interpreting spoken words based on context), said determination at least partly based on a result (e.g., a representation, either numeric or otherwise, provided by a machine inference, explicit user input, or other source, with or without human intervention) of at least a portion of the speech-facilitated transaction (e.g., withdrawing two hundred dollars from the automated teller machine device terminal by commanding the automated teller machine device using speech commands for at least part of the transaction).
  • a result e.g., a representation, either numeric or otherwise, provided by a machine inference, explicit user input, or other source, with or without human intervention
  • FIGs. 6A-6B depict, various implementations of operation 502, according to embodiments.
  • operation 502 may include operation 602 depicting managing adaptation data, wherein the adaptation data is correlated to at least one previous speech-facilitated transaction by the particular party.
  • Fig. 2 e.g., Fig.
  • FIG. 2A shows speech adaptation data related to at least one prior interaction between a particular party and a device regulating module 202 managing adaptation data (e.g., an ungrammatical utterance deletion algorithm), wherein the adaptation data is correlated to at least one previous speech-facilitated transaction by the particular party (e.g., the user's ungrammatical utterances in a previous transaction are recorded and analyzed, and stored in the adaptation data so that they can be removed more efficiently in future speech-facilitated transactions).
  • adaptation data e.g., an ungrammatical utterance deletion algorithm
  • operation 502 may include operation 604 depicting obtaining a reference to a location of adaptation data.
  • Fig. 2A shows address at which speech adaptation data is stored obtaining module 204 obtaining (e.g., receiving, acquiring, generating, retrieving, creating, or otherwise getting) a reference to a location (e.g., an Internet Protocol "IP" address of a computer that stores adaptation data) of adaptation data (e.g., a partial pattern tree model).
  • IP Internet Protocol
  • operation 502 may include operation 606 depicting testing an availability of the adaptation data at the location specified by the stored reference.
  • Fig. 2 e.g., Fig. 2A
  • operation 502 may include operation 608 depicting obtaining adaptation data, wherein the adaptation data is at least partly based on at least one speech interaction of a particular party.
  • Fig, 2, e.g., Fig. 2A shows speech adaptation data related to at least one aspect of a. particular party obtaining module 208 obtaining adaptation data (e.g., a regional dialect application algorithm), wherein the adaptation data is at least partly based on at least one speech interaction of a particular party (e.g., a user runs a program on a home computer that records the user's speech and trains the system, which learns the regional dialect of the user through the training program, and generates or modifies the regional dialect application algorithm).
  • adaptation data e.g., a regional dialect application algorithm
  • operation 502 may include operation 610 depicting controlling access to the adaptation data, wherein the adaptation data is at least partly- based on at least one speech interaction of a particular party.
  • Fig. 2 e.g., Fig.
  • FIG. 2A shows speech adaptation data related to at least one aspect of a particular party access monitoring module 210 controlling access (e.g., maintaining a permissions list, or determining one or more users/programs/devices/etc that are permitted to read, modify, update, and/or delete) to the adaptation data (e.g., a stochastic state transition network), wherein the adaptation data is at least partly based on at least one speech interaction of a particular party (e.g., a user giving speech commands to a speech-enabled Blu-ray player).
  • access e.g., maintaining a permissions list, or determining one or more users/programs/devices/etc that are permitted to read, modify, update, and/or delete
  • the adaptation data e.g., a stochastic state transition network
  • the adaptation data is at least partly based on at least one speech interaction of a particular party (e.g., a user giving speech commands to a speech-enabled Blu-ray player).
  • operation 502 may include operation 612 depicting managing adaptation data comprising data linking pronunciation of one or more phonemes by the particular party to one or more concepts.
  • Fig, 2, e.g., Fig, 2A shows speech adaptation data linking pronunciation of one or more syllables to one or more concepts regulating module 212 managing adaptation data comprising data linking pronunciation of one or more phonemes (e.g., phonemes comprising the word "twenty” by the particular party to one or more concepts (e.g., the concept of ha ving twenty of something, e.g., withdrawing twenty dollars, if the device being communicated with is an automated teller machine device).
  • phonemes e.g., phonemes comprising the word "twenty” by the particular party to one or more concepts
  • the concept of ha ving twenty of something e.g., withdrawing twenty dollars, if the device being communicated with is an automated teller machine device.
  • operation 502 may include operation 614 depicting obtaining a reference to a location of adaptation data.
  • Fig. 2A shows address of adaptation data generating module 214 obtaining (e.g., retrieving from memory) a reference to a location (e.g., a location within a cloud service, e.g., Amazon's cloud services) of adaptation data (e.g., a phrase completion algorithm).
  • a location e.g., a location within a cloud service, e.g., Amazon's cloud services
  • adaptation data e.g., a phrase completion algorithm
  • operation 502 may include operation 616 depicting providing the location of the adaptation data at particular intervals.
  • Fig. 2A shows address of adaptation data providing module 216 providing (e.g., broadcasting generally or transmitting to a specific device or devices) the location of the adaptation data (e.g., the phrase completion algorithm) at particular intervals (e.g., once a minute).
  • operation 502 may include operation 618 depicting obtaining a reference to a location of adaptation data.
  • Fig. 2B shows address of adaptation data generating module 218 obtaining (e.g., receiving, from a remote source, e.g., a remote server) a reference to a location (e.g., a web address, or a location on a networked drive) of adaptation data (e.g., a basic pronunciation adjustment algorithm).
  • a remote source e.g., a remote server
  • adaptation data e.g., a basic pronunciation adjustment algorithm
  • operation 502 may include operation 620 depicting providing the location of the adaptation data upon request.
  • Fig. 2 e.g., Fig. 2B
  • the adaptation data e.g., the basic pronunciation adjustment algorithm
  • a request e.g., from a user of the device, or from a signal received from another device, e.g., a target device, e.g., a speech-enabled digital video disc (“DVD”) player.
  • a target device e.g., a speech-enabled digital video disc (“DVD”) player
  • operation 502 may include operation 622 depicting managing an ordered list of verbal languages, wherein the ordered list of verbal languages corresponds to languages comprehended by the particular party and ranked by preference.
  • Fig. 2 e.g., Fig.
  • language list spoken by particular part ⁇ ' regulating module 222 managing an ordered list of verbal languages (e.g., "English, French, Spanish, Italian, German"), wherein the ordered list of verbal languages corresponds to languages comprehended by the particular party (e.g., the user might have English as a first language, be fluent in French, understand some Spanish, and be able to catch a few words of Italian and German) and ranked by preference (e.g., the user would rather speak English, then French, and then the other languages.
  • verbal languages e.g., "English, French, Spanish, Italian, German
  • operation 502 may include operation 624 depicting managing the ordered list, of verbal languages, wherein the ordered list of verbal languages corresponds to languages comprehended by the particular party and ranked by preference, and the ordered list of verbal languages is configured to be received by a device configured to generate a preferred language partly based on the ordered list of verbal languages and partly based on a device list of verbal languages comprehended by the device.
  • Fig. 2 e.g., Fig.
  • 2B shows language list spoken by particular party and configured to facilitate selection of transaction communication language regulating module 224 managing the ordered list of verbal languages (e.g., English, Spanish, and French), wherein the ordered list of verbal languages corresponds to languages comprehended by the particular party and ranked by preference, and the ordered list of verbal languages is configured to be received by a device (e.g., an automated ticket dispensing device in an airport in Spain) configured to generate a preferred language (e.g., Spanish) partly based on the ordered list of verbal languages (e.g., English, Spanish, and French) and partly based on a device list of verbal languages comprehended by the device ((e.g., Spanish, Basque, Italian, and French), so Spanish is selected as common to both lists and with a. higher "rank" than French).
  • a device e.g., an automated ticket dispensing device in an airport in Spain
  • a preferred language e.g., Spanish
  • a device list of verbal languages e.g.,
  • FIGs. 7A-7B depict various implementations of operation 504, according to embodiments.
  • operation 504 may include operation 702 depicting transmitting a pronunciation dictionary to a target device, in response to an indicator related to a speech-facilitated transaction of a particular party, wherein the adaptation data is correlated to at least one aspect of speech of the particular party.
  • Fig. 3 e.g.. Fig.
  • FIG. 3 A shows speech adaptation data including a list of one or more words and corresponding pronunciations transmission, in response to indication of a speech-facilitated transaction including a target device, facilitating module 302 transmitting a pronunciation dictionary to a target device (e.g., a motor vehicle control system), in response to an indicator related to a speech -facilitated transaction of a particular party (e.g., the user depresses a button on the steering wheel indicating that the user wants to give a speech command), wherein the adaptation data (e.g., an utterance ignoring algorithm) is correlated to at least one aspect of speech of the particular party (e.g., is correlated to prior pronunciations of words spoken by the user).
  • a target device e.g., a motor vehicle control system
  • an indicator related to a speech -facilitated transaction of a particular party e.g., the user depresses a button on the steering wheel indicating that the user wants to give a speech command
  • the adaptation data e.g., an
  • operation 504 may include operation 704 depicting transmitting a set of instructions for replacing a word frequency table with a modified word frequency table that reflects the particular party's word usage to a target device, in response to an indicator related to a speech-facilitated transaction of a particular party, wherein the adaptation data is correlated to at least one aspect of speech of the particular party.
  • Fig. 3 e.g., Fig.
  • FIG. 3A shows speech adaptation data including instructions for modifying a word frequency table transmission, in response to indication of a speech-facilitated transaction including a target device, facilitating module 304 transmitting a set of instructions for replacing a word frequency table with a modified word frequency table that reflects the particular party's word usage (e.g., with specific band names, e.g., "U2" higher in the word table based on the songs the user selects) to a target device (e.g., to a speech-enabled personal media player), in response to an indicator related to a speech- facilitated transaction (e.g., the user beginning to speak) of a particular party (e.g., the user of the speech-enabled personal media, player), wherein the adaptation data (e.g., the modified word frequency table) is correlated to at least one aspect of speech of the particular party (e.g., how many times or how often the user speaks one or more words).
  • the adaptation data e.g., the modified word frequency table
  • operation 504 may include operation 706 depicting transmitting a phrase completion algorithm to a target device, in response to an indicator related to a speech- facilitated transaction of a particular party, wherein the adaptation data, is correlated to at least one aspect of speech of the particular party.
  • Fig. 3 e.g., Fig.
  • FIG. 3A shows speech adaptation data including phrase completion algorithm transmission, in response to indication of a speech- facilitated transaction including a target, device, facilitating module 306 transmitting a phrase completion algorithm to a target device (e.g., a speech-enabled automated teller machine device), in response to an indicator related to a speech-facilitated transaction (e.g., the speech-enabled automated teller machine device detects that one or more particular words are being spoken by the user) of a particular party (e.g., a user waiting in line to use the speech-enabled automated teller machine device), wherein the adaptation data is correlated to at least one aspect of speech of the particular party (e.g., a specific way that the user pronounces the word "fifty”)-
  • a target device e.g., a speech-enabled automated teller machine device
  • an indicator related to a speech-facilitated transaction e.g., the speech-enabled automated teller machine device detects that one or more particular words are being spoken by the user
  • operation 504 may include operation 708 depicting facilitating transmission of the adaptation data to a target device, in response to an indication that the particular party intends to conduct a speech-facilitated transaction, wherein the adaptation data is correlated to at least one aspect of speech of the particular party.
  • Fig. 3 e.g., Fig.
  • 3A shows speech adaptation data transmission, in response to indication of intention to conduct a speech-facilitated transaction, facilitating module 308 facilitating transmission of the adaptation data (e.g., a noise level dependent filtration algorithm) to a target device (e.g., an automated drive-thru window), in response to an indication that the particular party (e.g., the user in her car) intends to conduct a speech-facilitated transaction (e.g., the user drives her car into a particular position detected by the system as being close enough to the automated drive-thru window to place an order), wherein the adaptation data is correlated to at least one aspect of speech of the particular party (e.g., correlated to previous speech of the user at other automated drive thru windows).
  • the adaptation data e.g., a noise level dependent filtration algorithm
  • operation 708 may include operation 710 depicting facilitating transmission of the adaptation data to a target device, in response to an indication received from the target device that the particular party intends to conduct a speech-facilitated transaction, wherein the adaptation data, is correlated to at least one aspect of speech of the particular party.
  • Fig. 3 e.g., Fig.
  • 3A shows speech adaptation data transmission, in response to receipt from target device of indication of intention to conduct a speech-facilitated transaction, facilitating module 310 facilitating transmission of the adaptation data (e.g., an emotion-based pronunciation adjustment algorithm) to a target device (e.g., a home security system), in response to an indication received from the target device (e.g., a base station of a home security system) that the particular party intends to conduct a speech-facilitated transaction (e.g., the particular party begins speaking (e.g., it can be determined later, or by another unit, whether the user is actually speaking to the target device, but, in an embodiment such as this one, the target device interprets the speech as the particular party intending to initiate a speech- facilitated transaction ), wherein the adaptation data is correlated to at least one aspect of speech of the particular party (e.g., speech patterns of the user that were detec ted o ver a period of time).
  • the adaptation data e.g., an emotion-based pronunciation adjustment algorithm
  • operation 710 may include operation 712 depicting facilitating transmission of the adaptation data to a target device, in response to an indication received from the target device that the particular party pressed a button on the target device.
  • operation 712 depicting facilitating transmission of the adaptation data to a target device, in response to an indication received from the target device that the particular party pressed a button on the target device.
  • Fig, 3A shows speech adaptation data transmission, in response to receipt from target device of indication that the particular party interacted with an input portion of the target device, facilitating module 312 facilitating transmission of the adaptation data (e.g., a syllable pronunciation database) to a target device (e.g., an automated teller machine device), in response to an indication received from the target device that the particular party pressed a button on the target device (e.g., a touchscreen button on a display of the automated teller machine device that says "begin").
  • the adaptation data e.g., a syllable pronunciation database
  • operation 710 may include operation 714 depicting facilitating transmission of the adaptation data to a target device, in response to an indication that the particular party is speaking to the target device.
  • Fig. 3, e.g., Fig. 3A shows speech adaptation data transmission, in response to receipt indication that the particular party is directing speech to the target device, facilitating module 314 facilitating transmission of the adaptation data (e.g., an accent-based pronunciation modification algorithm) to a target device (e.g., a speech-enabled laptop computer), in response to an indication that the particular party is speaking to the target device (e.g., in response to receiving a signal indicating that the microphone of the laptop has recorded sounds that have been identified as speech).
  • the adaptation data e.g., an accent-based pronunciation modification algorithm
  • operation 710 may include operation 716 depicting facilitating transmission of the adaptation data to a target device, in response to a request from, the target device for adaptation data correlated to at least one aspect of speech of the particular party.
  • Fig. 3 e.g., Fig.
  • FIG. 3A shows speech adaptation data transmission, in response to request for speech adaptation data, facilitating module 316 facilitating transmission of the adaptation data (e.g., a sentence diagramming path selection algorithm.) to a target device (e.g., a speech-enabled television set), in response to a request from, the target device for adaptation data correlated to at least one aspect of speech of the particular party (e.g., adaptation data derived from speech commands given to previous televisions owned by the user).
  • the adaptation data e.g., a sentence diagramming path selection algorithm.
  • operation 708 may include operation 718 depicting facilitating transmission of the adaptation data to a target device, in response to an indication received from a particular device associated with the particular party that the particular party intends to conduct a speech-facilitated transaction, wherein the adaptation data is correlated to at least one aspect of speech of the particular party.
  • Fig. 3 e.g., Fig.
  • FIG. 3B shows speech adaptation data transmission, in response to received indication of intention to conduct a speech-facilitated transaction from a particular device, facilitating module 318 facilitating transmission of the adaptation data (e.g., an uncommon word pronunciation guide) to a target device (e.g., a speech-enabled microwave oven), in response to an indication received from a particular device (e.g., a key ring with a chip and memory configured to send, receive, and store adaptation data) associated with the particular party (e.g., carried by the particular party) that the particular party intends to conduct a speech-facilitated transaction (e.g., commanding the microwave oven to defrost for five minutes), wherein the adaptation data is correlated to at, least one aspect of speech of the particular party (e.g., previous speech interactions of the particular party).
  • the adaptation data e.g., an uncommon word pronunciation guide
  • a target device e.g., a speech-enabled microwave oven
  • a particular device e.g., a key ring
  • operation 504 may include operation 720 depicting transmitting first adaptation data correlated to a first aspect of speech of the particular party to the target device.
  • Fig, 3 e.g., Fig. 3C
  • adaptatio data e.g., accent-based pronunciation modification algorithm
  • operation 504 may include operation 722 depicting transmitting second adaptation data correlated to a second aspect of speech of the particular party to the target device, prior to completion of the speech-facilitated transaction of the particular party.
  • Fig. 3 e.g., Fig.
  • second speech adaptation data related to second aspect of speech of particular party transmitting to target device module 322 transmitting second adaptation data (e.g., a different accent- based pronunciation modification algorithm) correlated to a second aspect of speech of the particular part (e.g., based on the user's previous pronunciation of a set of words such as "volume,” “source, ' ' "play,” and "fast- forward"), prior to completion of the speech-facilitated transaction of the particular party (e.g., before the speech-facilitated transaction ends).
  • second adaptation data e.g., a different accent- based pronunciation modification algorithm
  • operation 720 may include operation 724 depicting transmitting first adaptation data correlated to an accent of the particul ar party to the target device.
  • Fig. 3 e.g., Fig. 3C
  • a motor vehicle assistance/dialing system e.g., OnStar
  • operation 720 may include operation 726 depicting transmitting first adaptation data correlated to an expected vocabulary of the particular part ⁇ ' to the target device.
  • Fig. 3 e.g., Fig. 3C
  • pronunciation guidelines of words that might be used in the transaction e.g., "money,” “checking,” and “savings" of the particular party to the target device (e.g., an automated teller machine device).
  • operation 726 may include operation 728 depicting transmitting first adaptation data comprising a vocabulary of the particular party that is expected to be used during the speech-facilitated transaction to the target device.
  • Fig. 3 e.g., Fig. 3C
  • a vocabulary of the particular party e.g., a pronunciation of street names, city names, and proper names of places
  • the target device e.g., a personal navigation system mounted on a car windshield
  • operation 728 may include operation 730 depicting transmitting first adaptation data comprising a pronunciation dictionary of one or more words related to food items expected to be used during the speech-facilitated transaction to the target device, which is an automated drive -thru order taking device.
  • Fig, 3 e.g., Fig.
  • 3C shows first speech adaptation data related to one or more words related to food items estimated to be used in course of speech-facilitated transaction to target device module 330 transmitting first adaptation data comprising a pronunciation dictionary of one or more words related to food items (e.g., "hamburger,” “bacon,” “quarter-pounder”) expected to be used during the speech- facilitated transaction (e.g., ordering dinner at a fast food restaurant) to the target device, which is an automated drive -thru order taking device.
  • a pronunciation dictionary of one or more words related to food items e.g., "hamburger,” “bacon,” “quarter-pounder”
  • the target device which is an automated drive -thru order taking device.
  • operation 728 may include operation 732 depicting transmitting first adaptation data, comprising a pronunciation dictionary of one or more electronics and media words expected to be used during the speech- facilitated transaction to the target device, which is a speech-enabled media player.
  • first adaptation data comprising a pronunciation dictionary of one or more electronics and media words expected to be used during the speech- facilitated transaction to the target device, which is a speech-enabled media player.
  • Fig. 3 e.g.. Fig.
  • a pronunciation dictionary of one or more electronics and media words e.g., "play,” “stop,” “eject,” and “play the movie 'Inception'”
  • operation 720 may include operation 734 depicting transmitting first adaptation data correlated to a language spoken by the particular party.
  • Fig. 3 e.g., Fig. 3D
  • operation 720 may include operation 736 depicting transmitting first adaptation data based on previous speech-facilitated transactions by members of a group having at least one trait in common with the particular party.
  • Fig. 3 e.g., Fig. 3D
  • first speech adaptation data related to previous speech- facilitated transactions by members of a group having a particular trait transmitting to target device module 336 transmitting first adaptation data (e.g., a phoneme pronunciation database) based on previous speech-facilitated transactions by members of a group (e.g., people of Irish descent) having at least one trait (e.g., common descendants) in common with the particular part ⁇ '.
  • first adaptation data e.g., a phoneme pronunciation database
  • operation 736 may include operation 738 depicting transmitting first adaptation data, based on previous speech-facilitated transactions by members of a group originating from a same geographic area, as the particular party.
  • Fig. 3 e.g., Fig. 3D
  • first speech adaptation data related to previous speech-facilitated transactions by members of a. group from a particular community transmitting to target device module 338 transmitting first adaptation data (e.g., a basic pronunciation adjustment, algorithm) based on previous speech-facilitated transactions by members of a group originating from, a same geographic area (e.g., "the southern United States") as the particular party.
  • first adaptation data e.g., a basic pronunciation adjustment, algorithm
  • operation 722 may include operation 740 depicting transmitting second adaptation data correlated to a second aspect of speech of the particular party to the target device, prior to completion of the speech-facilitated transaction of the particular party, in response to a request from the target device.
  • Fig. 3 e.g., Fig.
  • FIG. 3C shows second speech adaptation data related to second aspect of speech of particular party transmitting in response to target device request module 340 transmitting second adaptation data (e.g., a regional dialect application algorithm) correlated to a second aspect of speech of the particular party (e.g., a specific accent of the user based on the user's prior speech interactions) to the target device (e.g., an automated ticket dispensing device), prior to completion of the speech- facilitated transaction of the particul ar party, in response to a request from the target de vice (e.g., the target device could not quickly interpret the speech using only the first adaptation data, so the target device requested second adaptation data.
  • second adaptation data e.g., a regional dialect application algorithm
  • operation 722 may include operation 742 depicting transmitting second adaptation data correlated to a second aspect of speech that is different than the first aspect of speech of the particular party to the target device, prior to completion of the speech-facilitated transaction of the particular party.
  • operation 742 depicting transmitting second adaptation data correlated to a second aspect of speech that is different than the first aspect of speech of the particular party to the target device, prior to completion of the speech-facilitated transaction of the particular party.
  • Fig. 3 e.g., Fig.
  • second speech adaptation data related to second aspect that is different than the first aspect transmitting to target device module 342 transmitting second adaptation data (e.g., a part-of-speech labeling algorithm) correlated to a second aspect of speech (e.g., observed speech patterns from prior speech interactions) that is different than the first aspect of speech of the particular party (e.g., accents of a group of people that, the particular party is part of) to the target device (e.g., the motor vehicle control system), prior to completion of the speech -facilitated transaction of the particul ar party.
  • second adaptation data e.g., a part-of-speech labeling algorithm
  • a second aspect of speech e.g., observed speech patterns from prior speech interactions
  • the target device e.g., the motor vehicle control system
  • operation 742 may include operation 744 depicting transmitting second adaptation data, wherein the second adaptation data is based on previous speech interactions of a second group of people, and the first adaptation data is based on previous speech interactions of a first, group of people, prior to completion of the speech-facilitated transaction.
  • Fig. 3 e.g., Fig. 3C
  • second adaptation data e.g., a second pronunciation adjustment algorithm, wherein the first adaptation data was a first, different
  • the second adaptation data is based on previous speech interactions of a second group of people (e.g., a people of particular ethnic background), and the first adaptation data is based on previous speech interactions of a first group of people (e.g., a group of people raised in a particular city), prior to completion of the speech-facilitated transaction.
  • a second group of people e.g., a people of particular ethnic background
  • the first adaptation data is based on previous speech interactions of a first group of people (e.g., a group of people raised in a particular city), prior to completion of the speech-facilitated transaction.
  • operation 744 may include operation 746 depicting transmitting second adaptation data, wherein the second adaptation data is based on previous speech interactions of a second group of people that is smaller than the first group of people, prior to completion of the speech-facilitated transaction.
  • Fig. 3 e.g., Fig.
  • 3C shows second speech adaptation data related to previous speech interactions of second group that is smaller than a first group related to the first speech adaptation data transmitting to target device module 346 transmitting second adaptation data (e.g., a second pronunciation adjustment algorithm designed to modify the first pronunciation adjustment algorithm), wherein the second adaptation data is based on previous speech interactions of a second group of people (e.g., people directly related to the particular party for which previous speech interactions were detected and analyzed) that is smaller than the first group of people (e.g., people who were raised in a particular city), prior to completion of the speech-facilitated transaction.
  • second adaptation data e.g., a second pronunciation adjustment algorithm designed to modify the first pronunciation adjustment algorithm
  • FIGs. 8A-8G depict various implementations of operation 506, according to embodiments.
  • operation 506 may include operation 802 depicting updating the adaptation data, said updating at least partly based on a result of at least a portion of the speech-facilitated transaction.
  • Fig. 4 e.g., Fig.
  • FIG. 4A shows speech adaptation data modification partly based on result of portion of speech- facilitated transaction module 402 updating (e.g., modifying at least a part of, even if it, is as little as changing the stored "last date used" in the adaptation data) the adaptation data (e.g., a sentence diagramming path selection algorithm), said updating at least, partly based on a result (e.g., how quickly the target device was able to process the speech using the sentence diagramming path selection algorithm) of at least a portion of the speech- facilitated transaction (e.g., giving a speech command to an automated teller machine device).
  • the adaptation data e.g., a sentence diagramming path selection algorithm
  • operation 506 may include operation 804 depicting determining not to update the adaptation data, said determination at least partly based on a result of at least a portion of the speech-facilitated transaction.
  • Fig. 4 e.g., Fig. 4A
  • speech adaptation data determining against modification partly based on result of portion of speech-facilitated transaction module 404 determining not to update the adaptation data (e.g., an uncommon word pronunciation guide), said determination at least partly based on a result (e.g., a user feedback indicating the user was "very pleased" with the transaction) of at least a portion of the speech-facilitated transaction (e.g., the user ordering a drink from an automated drink-dispensing machine device).
  • a result e.g., a user feedback indicating the user was "very pleased" with the transaction
  • a portion of the speech-facilitated transaction e.g., the user ordering a drink from an automated drink-dispensing machine device.
  • operation 506 may include operation 806 depicting determining whether to update the adaptation data, said determination at least partly based on a result of a completed speech-facilitated transaction.
  • Fig. 4 e.g., Fig. 4A, shows speech adaptation data potential modification determining partly based on result of finished speech-facilitated transaction module 406 determining whether to update the adaptation data, (e.g., a. non-lexical vocable removal algorithm), said determination at least partly based on a.
  • a completed speech- facilitated transaction e.g., the results of the completed transaction of interacting with a motor vehicle control system are transmitted to a remote server, and at a later date, the transaction is analyzed to determine how successful the algorithm was at removing non- lexical vocables
  • operation 506 may include operation 808 depicting determining whether to update the adaptation data, said determination at least partly based on a result of a beginning portion of the speech-fa cilitated transaction.
  • Fig. 4 e.g., Fig.
  • FIG. 4A shows speech adaptation data potential modification determining partly based on result of an initial portion of the speech-facilitated transaction module 408 determining whether to update the adaptation data (e.g., a speech disfluency detection algorithm), said determination at least, partly based on a result, of a beginning portion (e.g., a first two sentences of a transaction) of the speech- facilitated transaction (e.g., withdrawing money from a speech-enabled automated teller machine device).
  • a beginning portion e.g., a first two sentences of a transaction
  • the speech- facilitated transaction e.g., withdrawing money from a speech-enabled automated teller machine device.
  • operation 506 may include operation 810 depicting determining whether to update the adaptation data, wherein the speech- facilitated transaction comprises a first sentence and a second sentence, and said determination occurs after the first sentence and prior to the second sentence.
  • Fig. 4 e.g., Fig.
  • FIG. 4A shows speech adaptation data potential modification determining partly based on a first sentence of the speech facilitated transaction occurring prior to a second sentence of the speech- facilitated transaction module 410 determining whether to update the adaptation data (e.g., a context-based repaired utterance processing matrix), wherein the speech-facilitated transaction (e.g., commanding the motor vehicle control system) comprises a first sentence (e.g., "play Norah Jones") and a second sentence (e.g., "increase volume to twenty-five”), and said determination occurs after the first sentence and prior to the second sentence.
  • the adaptation data e.g., a context-based repaired utterance processing matrix
  • operation 506 may include operation 812 depicting determining whether to update the adaptation data, said determination made prior to completion of the speech-facilitated transaction.
  • Fig. 4 e.g., Fig. 4A
  • module 412 determining whether to update the adaptation data (e.g., a discourse marker detecting module), said determination ma de prior to completion of the speech-facilitated transaction (e.g., before a user completes his order at the automated drive -thru window).
  • operation 506 may include operation 814 depicting determining whether to update the adaptation data, said determination made after at least one word is spoken in the speech-facilitated transaction.
  • Fig. 4 e.g., Fig. 4A
  • speech adaptation data potential modification determining partly based on result of a portion of speech-facilitated transaction after at least one word is spoken by the particular party module 414 determining whether to update the adaptation data (e.g., a part-of-speech labeling algorithm), said determination made after at least one word is spoken in the speech- facilitated transaction (e.g., dictating a memorandum to a computer configured to receive speech as input).
  • operation 814 may include operation 816 depicting determining whether to update the adaptation data, said determination occurring prior to a particular word being spoken in the speech facilitated transaction.
  • Fig. 4 e.g.. Fig. 4A
  • speech adaptation data potential modification determining partly based on result of portion of speech-facilitated transaction and made prior to a particular word module 416 determining whether to update the adaptation data (e.g., a partial pattern tree model), said determination occurring prior to a particular word being spoken in the speech-facilitated transaction (e.g., prior to the word "goodbye” in a transaction that will be ended with the word "goodbye”)
  • the adaptation data e.g., a partial pattern tree model
  • operation 506 may include operation 818 depicting receiving a resul t of at least a portion of the speech-facilitated transaction.
  • Fig, 4, e.g., Fig. 4B shows portion of speech- facilitated transaction result acquiring module 418 receiving a result (e.g., an indication of whether interpretation of the portion of the transaction was successful, or how successful, or an estimate of how likely the device determines that the transaction was successful) of at least a portion (e.g., a first sentence spoken by the user and interpreted by the automated ticket dispensing device) of the speech- facilitated transaction (e.g., printing a plane ticket at the airport using a speech-enabled automated ticket dispensing device).
  • a result e.g., an indication of whether interpretation of the portion of the transaction was successful, or how successful, or an estimate of how likely the device determines that the transaction was successful
  • a portion e.g., a first sentence spoken by the user and interpreted by the automated ticket dispensing device
  • operation 506 may include operation 820 depicting determining whether to update the adaptation data, said determination at least partly based on the received result.
  • Fig. 4 e.g.. Fig. 4B
  • operation 818 may include operation 822 depicting receiving a result of at least a portion of the speech-facilitated transaction from the target device.
  • Fig. 4 e.g., Fig. 4B
  • portion of speech-facilitated transaction result acquiring from target device module 422 receiving a result (e.g., an estimate that the speech took seventy-two percent (72%) longer to complete than an average portion of the speech- facilitated transaction) of at least a portion of the speech-facilitated transaction (e.g., giving a command to a portable navigation system) from the target device (e.g., the portable navigation system sends the estimate).
  • a result e.g., an estimate that the speech took seventy-two percent (72%) longer to complete than an average portion of the speech- facilitated transaction
  • the target device e.g., the portable navigation system sends the estimate.
  • operation 818 may include operation 824 depicting receiving a result of at least a portion of the speech-facilitated transaction from the particular party.
  • Fig. 4 e.g., Fig. 4B
  • portion of speech-facilitated transaction result acquiring from particular party module 424 receiving a result (e.g., the transaction interpretation was gra ded "poor") of at lea st a portion of the speech- facilitated transaction (e.g., ordering a fountain soda from an automated drink preparing machine device) from the particular party (e.g., the user indicates that the transaction interpretation was "poor")
  • a result e.g., the transaction interpretation was gra ded "poor
  • the particular party e.g., the user indicates that the transaction interpretation was "poor”
  • operation 824 may include operation 826 depicting receiving a result of at least a portion of the speech- facilitated transaction from the particular party interacting with a particular device configured to manage the adaptation data.
  • Fig. 4 e.g., Fig.
  • FIG. 4B shows portion of speech-facilitated transaction result acquiring from particular device operated by particular party module 426 receiving a result of at least a portion of the speech-facilitated transaction (e.g., commanding a motor vehicle control system to lower the rear windows and open the sunroof) from the particular party interacting with a particular device (e.g., a smart key inserted into the car and configured to store, transmit, and receive adaptation data, and correlated to that particular driver) configured to manage the adaptation data (e.g., a word and/or syllable dependency parser).
  • a particular device e.g., a smart key inserted into the car and configured to store, transmit, and receive adaptation data, and correlated to that particular driver
  • the adaptation data e.g., a word and/or syllable dependency parser
  • operation 826 may include operation 828 depicting receiving a result of at least a portion of the speech-facilitated transaction from the particular party pushing a button on the particular device configured to manage the adaptation data.
  • Fig. 4 e.g., Fig.
  • FIG. 4B shows portion of speech-facilitated transaction result acquiring at least partially as a result of a pushed button of the particular device module 428 receiving a result (e.g., an indicator that the speech- facilitated transaction is fail ing) of at least a portion of the speech-facilitated transaction from the particular party (e.g., the user) pushing a button (e.g., a button that says "press here if the device you are communicating with is having trouble processing your speech") on the particular device (e.g., the user's smartphone) configured to manage the adaptation data (e.g., the smartphone manages the user's adaptation data, e.g., an ungrammatical utterance deletion algorithm).
  • a result e.g., an indicator that the speech- facilitated transaction is fail ing
  • the particular party e.g., the user
  • a button e.g., a button that says "press here if the device you are communicating with is having trouble processing your speech
  • the adaptation data e.g., the
  • operation 826 may include operation 830 depicting receiving a result of at least a portion of the speech-facilitated transaction from the particular party providing feedback to the particular device after an end of the speech- facilitated transaction.
  • Fig. 4 e.g., Fig. 4B
  • the particular party e.g., the user buying the ticket
  • providing feedback to the particular device e.g., filling out a survey displayed on a screen of a cellular smartphone
  • an end of the speech-facilitated transaction e.g., the survey is sent to the cellular smartphone and taken by the user after the speech-facilitated transaction has concluded.
  • operation 818 may include operation 832 depicting receiving an indication that the at least a portion of the speech-facilitated transaction was unsuccessful.
  • Fig. 4 e.g.. Fig. 4B
  • portion of speech-facilitated transaction result indicating less than complete success of the transaction acquiring module 432 receiving an indication that the at least a portion of the speech-facilitated transaction was unsuccessful (e.g., receiving an indication that, the first three sentences spoken by the user in the transaction were unable to be resolved into commands that the device understands).
  • operation 832 may include operation 834 depicting receiving an indication that the at least, a portion of the speech facilitated transaction was not successfully interpreted by the target device.
  • Fig. 4 e.g.. Fig. 4B
  • portion of speech-facilitated transaction result indicating incomplete or low confidence level interpretation of portion of transaction acquiring module 434 receiving an indication that at the at least a portion of the speech-facilitated transaction (e.g., giving a command to decrease the ambient temperature in a house control system) was not successfully interpreted (e.g., the command failed) by the target device (e.g., an automated teller machine device).
  • the target device e.g., an automated teller machine device
  • operation 818 may include operation 836 depicting receiving an indication that the target device has determined that the at least a portion of the speech-facilitated transaction was unsuccessful.
  • Fig. 4 e.g., Fig. 4C
  • portion of speech-facilitated transaction result indicating failure of the target device to carry out at least a.
  • portion of the speech- facilitated transa ction acquiring module 436 receiving an indication that the target device (e.g., the automated ticket dispensing device) has determined that the at least a portion of the speech-facilitated transaction (e.g., the first part of the transaction, in which the user is attempting to identify the city they want to travel to) was unsuccessful (e.g., the device was unable to pinpoint the city that was spoken by the user).
  • the target device e.g., the automated ticket dispensing device
  • operation 836 may include operation 838 depicting receiving an indication that the target device interpreted the at least a portion of the speech-facilitated transaction with an error rate above a particular threshold.
  • Fig. 4 e.g., Fig. 4C
  • portion of speech-facilitated transaction result indicating target-device determined error rate is above a.
  • threshold value acquiring module 436 receiving an indication that the target device (e.g., a speech-enabled copier device operating on an office enterprise network) interpreted the at least a portion of the speech-facilitated transaction (e.g., a command to change from "black and white” to "color”) with an error rate above a particular threshold (e.g., it, took four tries for the user's command to be understood).
  • the target device e.g., a speech-enabled copier device operating on an office enterprise network
  • operation 836 may include operation 840 depicting receiving an indication that the target device interpreted the at least a portion of the speech-facilitated transaction with a confidence rate below a particular threshold.
  • Fig. 4 e.g.. Fig. 4C
  • Fig. 4C shows portion of speech- facilitated transaction result indicating target-device determined confidence rate is below a threshold value
  • acquiring module 440 receiving an indication that the target device (e.g., a personal navigation system) interpreted the at least a portion of the speech-facilitated transaction (e.g., the name of the restaurant that the user is trying to find) with a confidence rate below a particular threshold (e.g., the confidence that the device identified the proper place is below twenty- five percent).
  • operation 818 may include operation 842 depicting receiving an indication that the particular party informed the target device that the at least a portion of the speech facilitated transaction was unsuccessful.
  • Fig. 4 e.g., Fig, 4C
  • FIG. 4C shows portion of speech- facilitated transaction result acquiring from particular party via target device module 442 receiving an indication that the particular party informed the target device (e.g., a speech-enabled automated teller machine device) that the at least a portion of the speech-facilitated transaction (e.g., the portion where the amount of money to be withdrawn is selected) was unsuccessful (e.g., the user presses a button indicating that none of the displayed amounts on the screen were the amount that the user stated).
  • the target device e.g., a speech-enabled automated teller machine device
  • operation 818 may include operation 844 depicting receiving a result of at least a portion of the speech- facilitated transaction, said result comprising a list of one or more words that were successfully interpreted.
  • Fig, 4, e.g., Fig. 4C shows portion of speech- facilitated transaction result including one or more interpreted words acquiring module 444 receiving a result (e.g., a list of one or more of the words that were part, of the transaction and successfully interpreted of at least a portion of the speech-facilitated transaction (e.g., a portion of an order at an automated drive -thru window).
  • a result e.g., a list of one or more of the words that were part, of the transaction and successfully interpreted of at least a portion of the speech-facilitated transaction (e.g., a portion of an order at an automated drive -thru window).
  • operation 818 may include operation 846 depicting receiving a result of at, least a portion of the speech-facilitated transaction, said result comprising a list of one or more words that were unsuccessfully interpreted.
  • Fig. 4 e.g., Fig.
  • portion of speech-facilitated transaction result including one or more words not completely interpreted acquiring module 446 receiving a result (e.g., a data structure containing one or more unsuccessfully interpreted words such as "prima facie") of at least a portion (e.g., one paragraph of) of the speech- facilitated transaction (e.g., a user dictating a legal memorandum using speech to a speech-enabled computer device operating on a home network).
  • a result e.g., a data structure containing one or more unsuccessfully interpreted words such as "prima facie”
  • the speech- facilitated transaction e.g., a user dictating a legal memorandum using speech to a speech-enabled computer device operating on a home network.
  • operation 818 may include operation 848 depicting receiving a resul t of at least a portion of the speech-facilitated transaction, said result comprising a pronunciation of one or more words spoken by the particular party during the speech- facilitated transaction.
  • Fig. 4 e.g., Fig.
  • portion of speech- facilitated transaction result including a representation of the particular party speaking one or more words of the speech-facilitated transaction acquiring module 448 receiving a result (e.g., a Waveform Audio File ("WAV") file of the user's pronunciation of one or more words that the device either could not interpret or that took more than one attempt to interpret) of at least a portion of the speech-facilitated transaction (e.g., giving a command to a.
  • a result e.g., a Waveform Audio File (“WAV") file of the user's pronunciation of one or more words that the device either could not interpret or that took more than one attempt to interpret
  • WAV Waveform Audio File
  • the home theater system comprising a pronunciation of one or more words spoken by the particular party (e.g., the words "play the movie 'Inception'") during the speech-facilitated transaction (e.g., the transaction including several steps to set the home theater settings, e.g., turning the sound to surround sound, decreasing the brightness and contrast, and tuning the video input to the Blu-ray player).
  • the particular party e.g., the words "play the movie 'Inception'”
  • the speech-facilitated transaction e.g., the transaction including several steps to set the home theater settings, e.g., turning the sound to surround sound, decreasing the brightness and contrast, and tuning the video input to the Blu-ray player.
  • operation 818 may include operation 850 depicting receiving a result of at least a portion of the speech-facilitated transaction, said result comprising a pronunciation of one or more words interpreted by a target device during the speech-facilitated transaction.
  • Fig. 4 e.g., Fig.
  • portion of speech-facilitated transaction result including a representation of the target device interpreting one or more words of the speech-facilitated transaction acquiring module 450 receiving a result of at, least a portion of the speech-facilitated transaction (e.g., giving a voice command to a speech-enabled television), said result comprising a pronunciation of one or more words (e.g., "power off') interpreted by a target device during the speech- facilitated transaction,
  • a result of at, least a portion of the speech-facilitated transaction e.g., giving a voice command to a speech-enabled television
  • said result comprising a pronunciation of one or more words (e.g., "power off') interpreted by a target device during the speech- facilitated transaction
  • operation 81 8 may include operation 852 depicting receiving a result of at least a portion of the speech-facilitated transaction, said result comprising a table of one or more words interpreted during the speech-facilitated transaction and a pronunciation of the one or more interpreted words by the particular party.
  • Fig. 4 e.g., Fig.
  • portion of speech- facilitated transaction result including a data structure configured to store one or more interpreted words of the speech-facilitated transaction and one or more corresponding representations of pronunciations of the one or more interpreted words acquiring module 452 receiving a result of at least a portion of the speech-facilitated transaction, said result comprising a table of one or more words interpreted during the speech-facilitated transaction and a pronunciation of the one or more interpreted words by the particular party (e.g., the user).
  • operation 506 may include operation 854 depicting receiving a result of the at least a portion of the speech- facilitated transaction.
  • Fig. 4 e.g., Fig. 4D
  • a result e.g., an indication of success of the interpretation of the speech
  • a microphone e.g., in a headset
  • operation 506 may include operation 856 depicting updating the adaptation data based on the result of the at least a portion of the speech- facilitated transaction.
  • Fig. 4 e.g.. Fig. 4D
  • speech adaptation data modifying at least partly based on received at least a portion of the result module 456 updating the adaptation data (e.g., a stochastic state transition network) based on the result of the at least a portion of the speech-facilitated transaction (e.g., giving a command through a microphone (e.g., in a headset) to a video game being played on a speech-enabled video game system).
  • adaptation data e.g., a stochastic state transition network
  • operation 506 may include operation 858 depicting transmitting updated adaptation data to the target device prior to completion of the speech-facilitated transaction.
  • Fig. 4 e.g., Fig. 4D
  • updated speech adaptation data e.g., a stochastic state transition network with at least one updated or changed state transition path
  • the target device prior to completion of the speech-facilitated transaction (e.g., prior to the user giving the last command in a string of commands for that game).
  • operation 506 may include operation 860 depicting receiving a result of the at least a portion of the speech-facilitated transaction.
  • Fig. 4 e.g., Fig. 4D
  • portion of speech-facilitated transaction result receiving module 456 receiving a result (e.g., an indication of whether the interpretation of the speech was successful) of the at least a portion of the speech- facilitated transaction (e.g., a portion of a user placing an order for a double cheeseburger, trench fries, and an iced tea with an automated drive-thru window)
  • operation 506 may include operation 862 depicting updating the adaptation data based on the result of the at least a portion of the speech- facilitated transaction.
  • Fig. 4 e.g., Fig. 4D
  • operation 506 may include operation 864 depicting receiving a result of the at least a portion of the speech- facilitated transaction.
  • Fig. 4 e.g., Fig. 4D
  • a result e.g., an indication of the target device's inference regarding whether the interpretation was successful
  • the at least a portion of the speech-facilitated transaction e.g., a user giving a command to an automated teller machine device.
  • operation 506 may include operation 866 depicting updating the adaptation d ata based on the result of the at least a portion of the speech- facilitated transaction.
  • Fig. 4 e.g., Fig. 4D
  • the adaptation data e.g., a phoneme pronunciation database
  • the result e.g., the inference made by the automated teller machine device regarding whether the interpretation was successful
  • PIN personal identification number
  • operation 506 may include operation 868 depicting transmitting a signal indicating that the adaptation data has been updated.
  • Fig. 4 e.g., Fig. 4E
  • operation 506 may include operation 870 depicting receiving update data from a further device regarding updating the adaptation data.
  • Fig. 4 e.g., Fig. 4E
  • update data e.g., data indicating how to modify a pronunciation dictionary
  • a further device e.g., a remote server
  • updating the adaptation data e.g., regarding modifying a. pronunciation of a. word in the pronunciation dictionary.
  • operation 506 may include operation 872 depicting updating the adaptation data based on the received update data.
  • Fig. 4D shows applying the potential modification data, received from the further device to the speech adaptation data module 472 updating the adaptation data (e.g., modifying the pronunciation dictionary) based on the received update data (e.g., the data indicating how to modif a pronunciation dictionary).
  • operation 506 may include operation 874 depicting receiving a result of the at least a portion of the speech-facilitated transa ction.
  • Fig. 4 e.g.. Fig. 4E
  • speech-facilitated transaction result portion receiving module 474 receiving a result, of the at least a portion (e.g., one command given to a home theater system) of the speech-facilitated transaction (e.g., giving a series of commands to a home theater system).
  • operation 506 may include operation 876 depicting transmitting the result of the at least a portion of the speech- facil itated transaction to a further location.
  • Fig. 4E shows speech-facilitated transaction result portion receiving module 474 receiving a result, of the at least a portion (e.g., one command given to a home theater system) of the speech-facilitated transaction (e.g., giving a series of commands to a home theater system).
  • operation 506 may include operation 876 depicting transmitting the result of the at least a portion of the speech- facil itated transaction
  • Fig. 4E shows speech- facilitated transaction result portion transmitting to device module 476 transmitting the result (e.g., the indication of whether the interpretation of the speech-facilitated transaction was successful) of the at least a portion of the speech-facilitated transaction (e.g., the one command given to the home theater system) to a further location (e.g., to a computer in the home that is networked to one or more of the devices of the home theater system).
  • the result e.g., the indication of whether the interpretation of the speech-facilitated transaction was successful
  • the at least a portion of the speech-facilitated transaction e.g., the one command given to the home theater system
  • a further location e.g., to a computer in the home that is networked to one or more of the devices of the home theater system.
  • operation 506 may include operation 878 depicting receiving, from the further location, update data comprising an indication of whether to update the adaptation data.
  • Fig. 4, e.g., Fig, 4E show r s determination data indicating wliether to modify the speech adaptation data receiving from device module 478 receiving, from the further location (e.g., the computer in the home networked to the one or more devices), update data comprising an indication of whether to update (e.g., whether to modify) the adaptation data (e.g., the phrase completion algorithm).
  • operation 876 may include operation 880 depicting transmitting the result of the at least a portion of the speech-facilitated transaction to a further location at which the adaptation data is also stored .
  • Fig. 4 e.g.. Fig. 4E, shows speech-facilitated transaction result portion transmitting to device that stores speech adaptation data module 480 transmitting the result (e.g., a numerical score indicating how efficiently the speech of the one command given to the home theater system was processed) of the at least a portio of the speech-facilitated transaction (e.g., the one command give to the home theater system) to a further location at which the adaptation data is also stored (e.g., a remote cloud server).
  • the result e.g., a numerical score indicating how efficiently the speech of the one command given to the home theater system was processed
  • the at least a portio of the speech-facilitated transaction e.g., the one command give to the home theater system
  • a further location at which the adaptation data is also stored e.g., a remote
  • operation 878 may include operation 882 depicting receiving, from the further location, update data further comprising instructions for updating the adaptation data.
  • Fig. 4 e.g., Fig. 4E
  • operation 506 may include operation 884 depicting receiving a result of the at least a portion of the speech- facilitated transaction.
  • Fig. 4 e.g., Fig. 4E
  • speech-facilitated transaction result portion reception module 484 receiving a result (e.g., a separate program operating on a computer and determining an effectiveness of the processing of speech done by the computer) of the at least a portion of the speech-facilitated transaction (e.g., carrying out a set of commands to customize an internet browser on a speech-enabled computer).
  • operation 506 may include operation 886 depicting determining to update the adaptation data, at least partly based on the result of the at least a portion of the speech-facilitated transaction.
  • Fig, 4, e.g., Fig. 4E shows speech adaptation data modification decision making partly based on speech-facilitated transaction result portion module 486 determining to update the adaptation data (e.g., a sentence diagramming path selection algorithm), at least partly based on the result of the at least a portion of the speech-facilitated transaction (e.g., the first command in the transaction to customize the browser, e.g., to load a customized home or start page).
  • the adaptation data e.g., a sentence diagramming path selection algorithm
  • operation 506 may include operation 888 depicting transmitting a. request to update the adaptation data to a further location.
  • Fig. 4 e.g.. Fig. 4E, shows request, to modify speech adaptation data transmitting to device module 488 transmitting a request to update the adaptation data (e.g., the sentence diagramming path selection algorithm) to a further location (e.g., to a remote server operated by the developer of the operating system on which the browser is running).
  • a further location e.g., to a remote server operated by the developer of the operating system on which the browser is running.
  • operation 506 may include operation 890 depicting receiving updated adaptation data from the further location.
  • Fig. 4 e.g., Fig. 4E
  • updated adaptation data e.g., a modified or changed sentence diagramming path selection algorithm
  • the further location e.g., from the remote server, which has modified the adaptation data based on many speech interactions of the user.
  • operation 506 may include operation 891 depicting receiving a result of the at least a portion of the speech-facilitated transaction, wherein the result indicates that the speech-facilitated transaction is moving too quickly for speech to be fully processed.
  • Fig. 4 e.g., Fig. 4E
  • operation 506 may include operation 893 depicting facilitating presentation of a visual or audible indicator to the particular party that indicates for the particular part ⁇ ' to slow down the speech-facilitated transaction.
  • Fig. 4 e.g., Fig.
  • FIG. 4E shows presentation of indicator to slow down facilitating module 493 facilitating presentation (e.g., either at a particular device, e.g., a smartphone, that is associated with the user, or a device with which the user is having a speech- facilitated transaction, e.g., an automated drive-thru window) of a visual or audible indicator (e.g., a beeping sound, or a yellow or red light) to the particular party that indicates for the particular party to slow down (e.g., either stop speaking for an interval, or slow the cadence of the user's voice) the speech-facilitated transaction.
  • a visual or audible indicator e.g., a beeping sound, or a yellow or red light
  • operation 506 may include operation 892 depicting receiving a result of a first portion of the speech- facilitated transaction.
  • Fig. 4 e.g., Fig. 4F
  • operation 506 may include operation 894 depicting updating the adaptation data at least partly based on the result of the first portion of the speech-facilitated transaction.
  • Fig. 4 e.g., Fig. 4F
  • speech adaptation data potential modification apply at least partly based on acquired result of the particular portion of speech-facilitated transaction 494 updating (e.g., changing one parameter of) the adaptation data (e.g., a regional dialect application algorithm) at least partly based on the result of the first portion of the speech-facilitated transaction (e.g., whether the device was able to efficiently identify the user and recognize the speech given to the device),
  • the adaptation data e.g., a regional dialect application algorithm
  • operation 506 may include operation 896 depicting transmitting updated adaptation data to the target device, said updated adaptation data configured to be applied by the target device prior to completion of a second portion of the speech-facilitated transaction.
  • Fig. 4 e.g., Fig.
  • modified speech adaptation data configured to be applied by the target device to a further portion of the speech- facilitated transaction transmitting module 496 transmitting updated adaptation data (e.g., the regional dialect, application algorithm with the modified parameter) to the target device (e.g., the automated ticket dispenser), said updated adaptation data configured to be applied by the target device (e.g., the automated ticket dispenser) prior to completion of a second portion of the speech-facilitated transaction (e.g., the user giving her destination city information).
  • updated adaptation data e.g., the regional dialect, application algorithm with the modified parameter
  • the target device e.g., the automated ticket dispenser
  • a second portion of the speech-facilitated transaction e.g., the user giving her destination city information
  • operation 894 may include operation 898 depicting changing an algorithm of the adaptation data at l east partly based on the result of the first portion of the speech-facilitated transaction.
  • Fig. 4 e.g., Fig. 4F
  • an algorithm e.g., switching from a regional dialect application algorithm to an accent-based pronunciation modification algorithm
  • operation 898 may include operation 801 depicting modifying at least one parameter of at least one algorithm of the adaptation data at least partly based on the result of the first portion of the speech-facilitated transaction.
  • Fig, 4 e.g.. Fig.
  • parameter of algorithm of speech adaptation data potential modification application at least partly based on acquired result of the particular portion of speech- facilitated transaction module 401 modifying at least one parameter of at least one algorithm (e.g., changing an amount of deamplification parameter of a noise level dependent filtration algorithm) of the adaptation data at least partly based on the result of the first portion of the speech-facilitated transaction (e.g., the result shows there were too many false positives of word fragments, so the parameter is adjusted).
  • at least one parameter of at least one algorithm e.g., changing an amount of deamplification parameter of a noise level dependent filtration algorithm
  • the adaptation data at least partly based on the result of the first portion of the speech-facilitated transaction (e.g., the result shows there were too many false positives of word fragments, so the parameter is adjusted).
  • operation 896 may include operation 803 depicting using a different algorithm of the adaptation data at least partly based on the result of the first portion of the speech-facilitated transaction.
  • Fig, 4, e.g., Fig. 4F shows different algorithm, of speech adaptation data selecting at, least partly based on acquired result of the particular portion of speech-facilitated transaction module 403 using a different, algorithm of the adaptation data (e.g., switching from a non-lexical vocable removal algorithm to a speech disfluency detection algorithm) at least partly based on the result of the first portion of the speech-facilitated transaction (e.g., the result shows that the algorithm was not doing anything useful because no non-lexical vocables were detected).
  • algorithm of the adaptation data e.g., switching from a non-lexical vocable removal algorithm to a speech disfluency detection algorithm
  • operation 894 may include operation 805 depicting modifying a pronunciation dictionary that is part of the adaptation data, said modifying at least partly based on the result of the first portion of the speech-facilitated transaction.
  • Fig. 4 e.g., Fig. 4G
  • operation 805 may include operation 807 depicting adding one or more words to the pronunciation dictionary that is part of the adaptation data., said modifying at least partly based on the result of the first portion of the speech- facilitated transaction.
  • Fig, 4 e.g., Fig.
  • 4G shows one or more word adding to pronunciation dictionary at least partly based on acquired result of the particular portion of speech-facilitated transaction module 807 adding one or more words (e.g., the words "Atlanta, Georgia”) to the pronunciation dictionary that is part of the adaptation data, said modifying at least partly based on the result of the first portion of the speech-facilitated transaction (e.g., the result indicated that the target device, e.g., the personal navigation system, could not initially recognize when the user said "Atlanta, Georgia,” so these words and the user's pronunciation were added to the adaptation data).
  • words e.g., the words "Atlanta, Georgia
  • the target device e.g., the personal navigation system
  • operation 805 may include operation 809 depicting adding one or more words to the pronunciation dictionary that is part of the adaptation data, said modifying at least partly based on the result of the first portion of the speech- facilitated transaction, said first portion comprising one or more words pronounced by the particular party and a corresponding pronunciation of the one or more words pronounced by the particular party, that are added to the pronunciation dictionary.
  • Fig. 4 e.g., Fig.
  • 4G shows one or more word adding to pronunciation dictionary at least, partly based on acquired result of the particular portion of speech-facilitated transaction that includes the one or more words module 809 adding one or more words to the pronunciation dictionary that is part of the adaptation data, said modifying at least partly based on the result of the first portion of the speech-facilitated transaction (e.g., the first portion is "entering the PIN" of a transaction of "withdrawing two hundred dollars from a checking account"), said first portion comprising one or more words pronounced by the particular party (e.g., "four,” “seven,” and “three") and a corresponding pronunciation of the one or more words pronounced by the particular party (e.g., the manner in which the user speaks the words “four,” “seven,” and “three”), that are added to the pronunciation dictionary.
  • the particular party e.g., "four,” “seven,” and "three
  • a corresponding pronunciation of the one or more words pronounced by the particular party e.g., the manner in which the user speaks the words “
  • operation 805 may include operation 81 1 depicting substituting, in the adaptation data, a new pronunciation dictionary for a previous pronunciation dictionary that was part of the adaptation data and previously applied in interpreting the first portion of the speech-facilitated transaction.
  • Fig. 4G shows pronunciation dictionary substitution at least partly based on acquired result of the particular portion of speech- facilitated transaction module 811 substituting, in the adaptation data, a new pronunciation dictionary for a. previous pronunciation dictionary that was part of the adaptation data and previously applied in interpreted in the first portion of the speech-facilitated transaction (e.g., the first part of an order at an automated drive -thru window).
  • operatio 894 may include operation 813 depicting modifying a path of a path selection algorithm of the adaptation data , said modifying at least partly based on the result of the first portion of the speech- facilitated transaction.
  • Fig. 4 e.g., Fig. 4H
  • operation 896 may include operation 815 depicting transmitting updated adaptation data to the target device, said updated adaptation data configured to be applied by the target device to interpret a second portion of the speech- facilitated transaction.
  • Fig. 4 e.g., Fig.
  • FIG. 4H shows modified speech adaptation data configured to be used to interpret, a further portion of the speech- facilitated transaction transmitting module 415 transmitting updated adaptation data to the target device (e.g., an automated teller machine device), said updated adaptation data configured to be applied by the target device (e.g., the automated teller machine device) to interpret a second portion (e.g., indicating an amount of money to withdraw) of the speech-facilitated transaction (e.g., withdrawing two hundred dollars from a savings account).
  • a second portion e.g., indicating an amount of money to withdraw
  • operation 896 may include operation 81 7 depicting transmitting updated adaptation data to the target device, said updated adaptation data configured to be applied by the target device to interpret a.
  • Fig. 4H shows modified speech adaptation data configured to be used to interpret a further portion of the speech- facilitated transaction directly after interpreting the particular portion transmitting module 417 transmitting updated adaptation data to the target device (e.g., a computer configured to receive speech input), said updated adaptation data configured to be applied by the target device to interpret a second portion of the speech-facilitated transaction (e.g., a second paragraph of a dictated memorandum), wherein no speech is interpreted between interpreting the first portion of the speech-facilitated transaction (e.g., the first paragraph of the dictated memorandum) and interpreting the second portion of the speech- facilitated transaction.
  • the target device e.g., a computer configured to receive speech input
  • said updated adaptation data configured to be applied by the target device to interpret a second portion of the speech-facilitated transaction (e.g., a second paragraph of a dictated memorandum)
  • no speech is interpreted between interpreting the first portion of the speech-facilitated transaction (e.g., the first paragraph of the dictate
  • operation 896 may include operation 819 depicting transmitting updated adaptation data to the target device.
  • Fig. 4 e.g., Fig. 4H
  • modified speech adaptation data transmitting module 419 transmitting updated adaptation data (e.g., an updated part-of-speech labeling algorithm) to the target device (e.g., a speech-enabled media player).
  • updated adaptation data e.g., an updated part-of-speech labeling algorithm
  • operation 896 may include operation 821 depicting receiving an indication from the target device that the updated adaptation data has been applied.
  • Fig. 4 e.g.. Fig. 4H
  • Fig. 8L operation 896 may include operation 823 depicting transmitting updated adaptation data to the target device.
  • Fig. 4H shows indication of receipt, of modified speech adaptation data receiving module 421 receiving an indication (e.g., receiving a signal transmitted from) from the target device (e.g., the speech-enabled media player) that the updated adaptation data has been applied (e.g., that the updated adaptation data has replaced the adaptation data, or the existing adaptation data has been modified as instructed by
  • modified speech adaptation data transmitting module 423 transmitting updated adaptation data (e.g., a set of proper noun pronunciations, e.g., city names, that have been modified based on new user pronunciations) to the target device (e.g., a vehicle-based navigation system).
  • updated adaptation data e.g., a set of proper noun pronunciations, e.g., city names, that have been modified based on new user pronunciations
  • the target device e.g., a vehicle-based navigation system
  • operation 896 may include operation 825 depicting receiving an indication from the target device that the updated adaptation data has been applied.
  • Fig. 4 e.g., Fig. 41
  • the updated adaptation data e.g., a set of proper noun pronunciations, e.g., city names, that have been modified based on new user pronunciations
  • the target device e.g., a vehicle -based navigation system
  • the updated adaptation data e.g., a set of proper noun pronunciations, e.g., city names, that have been modified based on new user pronunciations
  • operation 896 may include operation 827 depicting facilitating a presentation of an indicator indicating that the second portion of the speech- facilitated transaction is ready to occur.
  • Fig, 4, e.g., Fig, 41 shows indication of readiness to complete further portion of speech-facilitated transaction presenting module 427 facilitating a presentation (e.g., a visual indication, e.g., a light on the device is activated, or an audible beep is emitted) of an indicator indicating that, the second portion of the speech-facilitated transaction is ready to occur (e.g., the user's cellular device beeps when the vehicle-based navigation system receives the updated adaptation data).
  • a presentation e.g., a visual indication, e.g., a light on the device is activated, or an audible beep is emitted
  • an indicator indicating that, the second portion of the speech-facilitated transaction is ready to occur (e.g., the user's cellular device beeps when the vehicle-based navigation
  • operation 827 may include operation 829 depicting displaying a message on a screen of the particular device indicating that the second portion of the speech-facilitated transaction is ready to occur.
  • Fig. 4 e.g., Fig. 41
  • a message e.g. "continue speaking now”
  • the particular device e.g., a user's universal remote control
  • operation 827 may include operation 831 depicting facilitating a display of a message on a screen of the target device indicating that the second portion of the speech- facilitated transaction is ready to occur.
  • Fig. 4 e.g., Fig.
  • FIG. 41 shows message indicating readiness of further portion of speech- facilitated transaction display on target device facilitating module 431 facilitating a display of a message on a screen of the target device (e.g., sending a signal to the automated drive-thru window that instructs the automated drive-thru window to display the words "please continue your order") indicating that the second portion of the speech- facilitated transaction (e.g., the second order of a double bacon cheeseburger and fries) is ready to occur.
  • the second portion of the speech- facilitated transaction e.g., the second order of a double bacon cheeseburger and fries
  • trademarks e.g., a. word, letter, symbol, or device adopted by one manufacturer or merchant and used to identify and/or distinguish his or her product from those of others.
  • Trademark names used herein are set forth in such language that makes clear their identity, that distinguishes them from common descriptive nouns, that have fixed and definite meanings, or, in many if not all cases, are accompanied by other specific identification using terms not covered by trademark.
  • trademark names used herein have meanings that are well- known and defined in the literature, or do not refer to products or compounds for which knowledge of one or more trade secrets is required in order to divine their meaning.

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Artificial Intelligence (AREA)
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Abstract

Selon l'invention, des méthodes et systèmes mis en œuvre informatiquement consistent à gérer des données d'adaptation, les données d'adaptation étant corrélées avec au moins un aspect de la parole d'une partie donnée, faciliter la transmission des données d'adaptation à un dispositif cible, en réponse à un indicateur associé à une transaction facilitée par la parole d'une partie donnée, les données d'adaptation étant corrélées avec au moins un aspect de la parole de la partie donnée, et déterminer s'il faut mettre à jour les données d'adaptation, ladite détermination étant basée au moins en partie sur un résultat d'au moins une partie de la transaction facilitée par la parole. En supplément de ce qui précède, d'autres aspects sont décrits dans les revendications, les figures et le texte.
EP13808577.4A 2012-06-29 2013-06-28 Méthodes et systèmes de gestion de données d'adaptation Withdrawn EP2867889A4 (fr)

Applications Claiming Priority (13)

Application Number Priority Date Filing Date Title
US13/538,866 US20130325447A1 (en) 2012-05-31 2012-06-29 Speech recognition adaptation systems based on adaptation data
US13/538,855 US9495966B2 (en) 2012-05-31 2012-06-29 Speech recognition adaptation systems based on adaptation data
US13/564,647 US9620128B2 (en) 2012-05-31 2012-08-01 Speech recognition adaptation systems based on adaptation data
US13/564,650 US20130325449A1 (en) 2012-05-31 2012-08-01 Speech recognition adaptation systems based on adaptation data
US13/564,651 US9899026B2 (en) 2012-05-31 2012-08-01 Speech recognition adaptation systems based on adaptation data
US13/564,649 US8843371B2 (en) 2012-05-31 2012-08-01 Speech recognition adaptation systems based on adaptation data
US13/609,142 US20130325451A1 (en) 2012-05-31 2012-09-10 Methods and systems for speech adaptation data
US13/609,143 US9305565B2 (en) 2012-05-31 2012-09-10 Methods and systems for speech adaptation data
US13/609,145 US20130325453A1 (en) 2012-05-31 2012-09-10 Methods and systems for speech adaptation data
US13/609,139 US10431235B2 (en) 2012-05-31 2012-09-10 Methods and systems for speech adaptation data
US13/662,228 US10395672B2 (en) 2012-05-31 2012-10-26 Methods and systems for managing adaptation data
US13/662,125 US9899040B2 (en) 2012-05-31 2012-10-26 Methods and systems for managing adaptation data
PCT/US2013/048640 WO2014005055A2 (fr) 2012-06-29 2013-06-28 Méthodes et systèmes de gestion de données d'adaptation

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HK1207199A1 (en) 2016-01-22
WO2014005055A3 (fr) 2014-03-06
EP2867889A4 (fr) 2016-03-02

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