US20070124147A1 - Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems - Google Patents

Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems Download PDF

Info

Publication number
US20070124147A1
US20070124147A1 US11291231 US29123105A US2007124147A1 US 20070124147 A1 US20070124147 A1 US 20070124147A1 US 11291231 US11291231 US 11291231 US 29123105 A US29123105 A US 29123105A US 2007124147 A1 US2007124147 A1 US 2007124147A1
Authority
US
Grant status
Application
Patent type
Prior art keywords
speech recognition
recognition system
user
vocabulary
word
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.)
Abandoned
Application number
US11291231
Inventor
Ramesh Gopinath
Dimitri Kanevsky
Mahesh Viswanathan
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.)
Nuance Communications Inc
Original Assignee
International Business Machines Corp
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

Links

Images

Classifications

    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/08Speech classification or search
    • G10L15/18Speech classification or search using natural language modelling
    • G10L15/183Speech classification or search using natural language modelling using context dependencies, e.g. language models
    • G10L15/19Grammatical context, e.g. disambiguation of the recognition hypotheses based on word sequence rules
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/06Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/063Training
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/08Speech classification or search
    • G10L15/18Speech classification or search using natural language modelling
    • G10L15/183Speech classification or search using natural language modelling using context dependencies, e.g. language models
    • GPHYSICS
    • G10MUSICAL INSTRUMENTS; ACOUSTICS
    • G10LSPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00Speech recognition
    • G10L15/06Creation of reference templates; Training of speech recognition systems, e.g. adaptation to the characteristics of the speaker's voice
    • G10L15/063Training
    • G10L2015/0631Creating reference templates; Clustering

Abstract

The present invention concerns methods and apparatus for identifying and assigning meaning to words not recognized by a vocabulary or grammar of a speech recognition system. In an embodiment of the invention, the word may be in an acoustic vocabulary of the speech recognition system, but may be unrecognized by an embedded grammar of a language model of the speech recognition system. In another embodiment of the invention, the word may not be recognized by any vocabulary associated with the speech recognition system. In embodiments of the invention, at least one hypothesis is generated for an utterance not recognized by the speech recognition system. If the at least one hypothesis meets at least one predetermined criterion, a word or more corresponding to the at least one hypothesis is added to the vocabulary of the speech recognition system. In other embodiments of the invention, before adding the word to the vocabulary of the speech recognition system, the at least one hypothesis may be presented to the user of the speech recognition system to determine if that is what the used intended when the user spoke.

Description

    TECHNICAL FIELD
  • The invention concerns methods and apparatus for use in speech recognition systems and more particularly concerns methods and apparatus for identifying and assigning meaning to new words and utterances. The new words and utterances may be known beforehand, but used in a new way unknown to an embedded grammar of a language model incorporated in a speech recognition system, or may be totally unknown beforehand from any perspective to a speech recognition system.
  • BACKGROUND
  • Speech recognition systems are finding increasing use, particularly in voice-controlled user interfaces. Voice-controlled user interfaces are familiar to anyone who performs banking and credit card transactions by telephone. In the past, telephonic banking and credit card service transactions were performed either through interaction with a human agent or by using a keypad of a telephone; now, with increasing frequency telephonic banking and credit card service transactions may be performed using voice commands.
  • Voice-activated user interfaces are also finding increasing use in portable electronic devices like cellular telephones and personal digital assistants (“PDAs”) with telephonic capabilities. For example, in cellular telephones with voice-activated user interface capability, a user can enter a voice command “Call Bob Smith” in order to initiate a telephone call to a target person (“Bob Smith”). This eliminates the need for the user to enter a telephone number, or to access a contact list containing the telephone number, thereby saving keystrokes. The elimination of keystrokes often enables hands-free modes of operation, which is particularly advantageous when the telephone call is initiated by someone operating an automobile. There is increasing pressure to restrict the operation of cellular telephones by drivers of automobiles, particularly cellular telephones that require hand operation.
  • Thus, the ability to initiate an operation (e.g., a telephone call) by issuing a voice command to a voice-controlled user interface is particularly advantageous because it saves time and effort previously expended by entering commands using keys or other hand-operated input devices. This advantage ends, though, as soon as a user enters a command not recognized by a speech recognition system associated with a voice-controlled user interface. In such circumstances, a user is often thrust back to old, more tedious modes of operation where a command has to be entered using a combination of keystrokes.
  • In such situations, where a cellular telephone user is seeking to initiate a telephone call, the user would either have to enter the telephone number directly, or add it to a contact list. Since users of productivity-enhancement devices like cellular telephones and PDAs value the ability of these devices to “grow” with the user by, for example, being able to record and save an extensive and ever-expanding contact list, the fact that this ability may only be partially implemented (if at all) through voice commands is viewed as a particular limitation of voice-activated user interface systems incorporated in such devices. If a user has an extensive contact list, the user might not even initiate a telephone call using the voice command feature, because the user might forget whether the person to be called is even in the contact list and thus capable of being recognized by a voice-activated user interface operating in combination with the contact list.
  • A further problem is apparent in this description of the prior art. In conventional speech recognition systems, the vocabularies and grammars are fixed. Accordingly, when the user is thrust back upon a keystroke-mode of operation in order to enter new commands, the user will have to enter the new commands with keystrokes every time the new commands are to be performed, since the vocabularies and grammars are fixed. There is no benefit to the speech recognition system associated with the user giving meaning to a command unrecognized by the speech recognition system using keystrokes, since the information entered using keystrokes does not modify the capabilities of the speech recognition system.
  • Accordingly, those skilled in the art desire speech recognition systems with the ability to “grow.” In particular, those skilled in the art desire speech recognition systems with the ability to identify new words previously unknown to the speech recognition system and to add them to one or more vocabularies and grammars associated with the speech recognition system. In addition, those skilled in the art desire voice activated user interfaces with the ability to learn new commands. Further, when it is necessary to enter commands using keystrokes, those skilled in the art seek speech recognition systems that can be re-programmed though interaction with keys, keyboards, and other command entry controls of an electronic device, so that the speech recognition system benefits from the efforts expended in such activities.
  • SUMMARY OF THE PREFERRED EMBODIMENTS
  • The foregoing and other problems are overcome, and other advantages are realized, in accordance with the following embodiments of the present invention.
  • A first embodiment of the present invention comprises a signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus of a computer system to perform speech recognition operations, the speech recognition operations comprising: detecting at least a target word known to an acoustic vocabulary but unknown to an embedded grammar of a language model of the speech recognition system; assigning a language model probability to the target word; calculating a sum of an acoustic and language model confidence score for the target word and words already included in the embedded grammar of the language model; and if the sum of the acoustic and language model probability for the target word is greater than the sum of the acoustic and language model probability for the words already included in the embedded grammar, adding the target word to the language model.
  • A second embodiment of the present invention comprises a signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus of a computer system to perform speech recognition operations, the speech recognition operations comprising: detecting an utterance having a low acoustic score within an acoustic vocabulary of the speech recognition system indicating that the utterance may correspond to an out-of-vocabulary word; generating at least one new word hypothesis comprised of at least one of a phone- or syllable sequence using confidence scores derived from probabilities contained in a database of viable phone and syllable sequences; and if the at least one new word hypothesis meets a pre-determined criterion, adding a word corresponding to the at least one new word hypothesis to the vocabulary of the speech recognition system.
  • A third embodiment of the present invention comprises a signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus of a computer system to perform speech recognition operations in a speech recognition system, the speech recognition operations comprising: detecting an utterance not recognized by at least a first one of an acoustic vocabulary, embedded grammar, and viable phone/syllable sequence library of the speech recognition system; generating at least one hypothesis for the utterance, wherein the hypothesis is based on information derived from a second one of an acoustic vocabulary, embedded grammar and viable phone/syllable sequence library of the speech recognition system; calculating a confidence score for the at least one hypothesis and for members of the first one of the acoustic vocabulary, embedded grammar and viable phone/syllable sequence library of the speech recognition system; comparing the confidence scores calculated for the at least one hypothesis and for members of the first one of the acoustic vocabulary, embedded grammar and viable phone/syllable sequence library of the speech recognition system; and adding information to the first one of an acoustic vocabulary, embedded grammar and viable phone/syllable sequence corresponding to the hypothesis if a pre-determined criterion based on the comparison is met.
  • A fourth embodiment of the present invention comprises a speech recognition system comprising: a speech input for receiving speech from a user of the speech recognition system; an open set comprised of at least one open vocabulary and at least one open embedded grammar associated with a language model implemented in the speech recognition system; a hierarchical mapping system for identifying utterances not recognized by at least one of the open vocabulary and open embedded grammar of the speech recognition system; for generating hypotheses for the unrecognized utterances using confidence scores based at least in part on one of viable phone/syllable sequence information, acoustic vocabulary information and grammar information; and for adding information corresponding to the hypotheses to at least one of the open vocabulary and embedded grammar of the speech recognition system if a pre-determined criterion is met; and a confidence score system for generating confidence scores for use by the hierarchical mapping system.
  • In conclusion, the foregoing summary of the alternate embodiments of the present invention is exemplary and non-limiting. For example, one of ordinary skill in the art will understand that one or more aspects or steps from one alternate embodiment can be combined with one or more aspects or steps from another alternate embodiment to create a new embodiment within the scope of the present invention.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The foregoing and other aspects of these teachings are made more evident in the following Detailed Description of the Preferred Embodiments, when read in conjunction with the attached Drawing Figures, wherein:
  • FIG. 1 is a block diagram depicting a system embodying several aspects of the present invention;
  • FIG. 2 is a block diagram depicting in greater detail the hierarchical mapping system of FIG. 1;
  • FIG. 3 is a block diagram depicting a phone/syllable mapper made in accordance with the present invention;
  • FIG. 4 is a block diagram depicting a user behavioral biometrics detector made in accordance with the present invention; and
  • FIG. 5 is a flow chart depicting a method operating in accordance with the present invention.
  • DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS
  • As introduction, an aspect of the present invention will be described to illustrate problems encountered in the prior art and how the present invention solves them. Embodiments of the present invention are generally operative in automated, electronic speech recognition systems that are used in electronic devices with speech input capability such as, for example, telephones. The speech recognition systems typically operate in such electronic devices as part of a voice-activated user interface. Before the electronic device can take action in response to a user command, the speech recognition system has to parse the speech utterance comprising the command and assign meaning to the speech utterance. In prior art devices, users are required to operate within relatively narrow categories of vocabulary and grammar when interacting with a speech recognition system, because conventional speech recognition systems are fixed in capability. The speech recognition systems of the prior art have fixed vocabularies and grammars, meaning that if a speech utterance is not in a speech recognition system's vocabulary and grammar, no action or possibly even an incorrect action will be taken by the voice-activated user interface. This occurs because the speech utterance is unknown to the speech recognition system associated with the voice activated user interface.
  • Accordingly, an aspect of the present invention provides a speech recognition system with open vocabularies and grammars, allowing the speech recognition system to be programmed with new words and grammatical constructs (such as, for example, commands) through interaction with a user. As a result of these interactions, a voice-activated user interface with which the speech recognition system is associated can be programmed to perform new actions. To illustrate the operation of an aspect of the invention an example will be provided. Assume a user is interacting with a voice-activated user interface that is incorporated in a telephone, and speaks a command “Call Morita-san”. “Morita” is a Japanese surname, and “Morita-san” is a way one named “Morita” may be addressed in Japanese. The speech recognition system is programmed to recognize the command “Call ______”, and also is programmed to recognize certain names and telephone numbers that are used in combination with the “Call ______” command. However, in this particular example, the speech recognition system is initially not programmed to recognize the name “Morita-san”, nor has the user heretofore uttered the words “Morita-san” in combination with the command “Call ______”. Accordingly, in one aspect of the present invention, the speech recognition system generates a phonetic sequence hypothesis for “Morita-San” having a high degree of probability; presents the hypothesis to the user for confirmation, including spelling; and after receiving confirmation (and possibly even a spelling correction) adds the word “Morita-San” to an embedded grammar associated with the “Call ______” command. In various embodiments of the invention, additional steps may be performed. For example, the user may associate a specific telephone number with the word “Morita-san” as it is being added to the embedded grammar of the speech recognition system. Once “Morita-san” has been added to the embedded grammar and the telephone number has been associated with the new word “Morita-san”, the next time the speech recognition system hears the command “Call Morita-san” it will automatically call the telephone number associated with “Morita-san”.
  • In variants of this embodiment, confidence scores may be assigned using additional information besides, for example, phonetic or grammar information. Higher-level models based on semantic and context information may be used in combination with phonetic and grammar information to identify unknown words using confidence scores. For example, regarding context, the speech recognition system may take into consideration what actions the user of the speech recognition system had been performing prior to speaking the unrecognized word. These actions provide context information which may assist the speech recognition system in assigning meaning to the unrecognized word.
  • In another embodiment of the invention, the speech recognition system would automatically poll the user of the speech recognition system to enter by keystrokes the information associated with the unrecognized command. Assume the user spoke the same sequence as in the preceding example, “Call Morita-san”, and the system did not recognize either the grammatical construct “Call ______” or the name “Morita-san”. In this embodiment of the invention, the voice-recognition system would ask the user to illustrate the command by keystrokes and provide the name by keystrokes. Accordingly, after entry of the illustrative example, the speech recognition system would then recognize that the “Call ______” construct corresponds to an instruction to perform a telephone call. In addition, after entry of the name “Morita-san” (and possibly an associated telephone number), the speech recognition system would recognize “Morita-san” as someone to be called at a specific telephone number.
  • Further embodiments of the present invention implement additional features that may be used in combination with the functionality associated with the foregoing aspects of the present invention. For example, often a user of a speech recognition system provides biometric cues identifying when the user is introducing a new word. The user may slow down her speech to emphasize a word, may speak more loudly to emphasize a word, or may pause to emphasize a word. These actions may be used alone or in combination with physical gestures to emphasize a word. Further embodiments of the present invention employ audio and visual biometric measuring systems to help identify when a user of a speech recognition system is speaking a new word.
  • Now further aspects of the present invention, and the problems they overcome, will be described in greater detail. There are two typical situations encountered in a speech recognition system with respect to new words. In a first situation, the speech recognition system recognizes a word as a valid phonetic sequence known to at least one acoustic vocabulary of the speech recognition system. However, the word is used in new way not recognized by an embedded grammar of a language model incorporated in the speech recognition system. “Embedded grammar” and “language model” are concepts and means for implementing a speech recognition system that generally refer to the fact that a speech recognition system recognizes and assigns meaning to not only words, but to combinations of words. In a voice-activated user interface incorporating a speech recognition system, “embedded grammar” and “language model” refer to the functionality of the speech recognition system that recognizes both responses to queries initiated by the voice-activated user interface, and to commands entered by a user of the voice-activated user interface. So in the first example, a word that is recognized as a valid phonetic sequence is nonetheless used in a such a way that the speech recognition system cannot assign meaning to the utterance incorporating the word, since the word is used in a new way. A typical example would be encountered when a word that is recognized by a voice-activated user interface as a valid phonetic sequence is used in a command, wherein the embedded grammar functionality which ordinarily detects the command is not programmed to recognize and assign meaning to the command when the command incorporates the new word. In one aspect of the present invention various methods and apparatus are provided that enable an embedded grammar of a speech recognition system to “grow” by adding new words to the embedded grammar.
  • In a more general situation, a sequence of sounds corresponding to one or more words spoken by a user of a speech recognition system may be unknown to any vocabulary or language model of the speech recognition system. In this aspect of the present invention, various methods and apparatus are provided that enable a speech recognition system to grow both by adding previously unknown words to one or more vocabularies of the speech recognition system, and by adding new grammatical constructs (such as, for example, new commands) to an embedded grammar of a language model incorporated in a speech recognition system.
  • Embodiments of the present invention responding to the first circumstance identified above—where a known word is used in a new, unrecognized context—are handled in the following manner. Generally, an embedded grammar incorporated in a language model of a speech recognition system operating in accordance with the invention is designed to expand by accommodating new uses for words recognized by other aspects of the speech recognition system (such as phonetic vocabularies).
  • A conventional embedded grammar operates as follows when a word included in the grammar is spoken:
      • Construct: {W1} {W2}
        • Prepare list of acceptable Li's
        • L1, L2, . . . are all list items—part of an embedded grammar
        • L1, . . . Ln are all equi-probable (to a first degree of approximation)
        • For example, Call <name>, where name may be a list of 50 proper names
        • Phrase score for {W1} {W2} {Li}
        • =Acoustic score (Li)+Language Model Score (Li|W1W2)
          As is apparent, a particular word Li having the highest sum for acoustic score and language model score is deemed to be the most likely hypothesis for the word intended by a speaker. No accommodation is made in conventional methods for words unrecognized by the speech recognition system.
  • In contrast, in methods and apparatus of the present invention, embedded grammars and language models of a speech recognition can expand to incorporate words that are recognized by other aspects of the speech recognition system (such as, for example a phonetic vocabulary), but which are not recognized by a particular embedded grammar as a valid option. A method of the present invention operates in the following manner:
      • (‘U’ (Word actually spoken) is not in an embedded grammar) Construct: {W1} {W2}
        • “Create” an empty list item and assign it a non-zero probability, P{U}<P{Li}
        • Word (‘U’) recognized by other aspects of speech recognition system but not by embedded grammar has a small probability allowing grammar room to expand
        • For example, “Go to <city not in embedded grammar>
        • P{U}<P{Li}, but Acoustic Score (U)+Language Model
        • Score (U)>Acoustic Model Score (Li)+Language Model Score (Li)
          In this method of the present invention, the sum of the acoustic and language model scores will favor the word recognized by other aspects of the speech recognition system (such as a phonetic vocabulary) but not by the embedded grammar over words that are recognized by the embedded grammar. This results from the fact that none of the words initially in the embedded grammar sound like the word actually spoken. Alternatively, the word not in the embedded grammar is recognized phonetically with a high degree of probability since the word is in at least one phonetic vocabulary of the speech recognition system. Accordingly, the speech recognition system concludes that the most likely hypothesis is that the speaker intended to use the new word in, for example, the command spoken, as opposed to any words recognized by the embedded grammar.
  • A method operating in accordance with this aspect of the present invention may be followed by additional steps. For example, the speech recognition system may synthesize a hypothesis corresponding to the utterance spoken by the speaker and play it to the speaker using the word not initially in the embedded grammar but incorporated in some other vocabulary or grammar of the speech recognition system. In such an instance the system would seek confirmation from the speaker that the word is what the speaker intended. As part of these additional steps, a baseform may be generated so that pronunciation can be confirmed.
  • In the other situation described above where an utterance is unrecognized by any vocabulary or grammar of a speech recognition system, the present invention operates on phone sequences to generate hypotheses for a word or combinations of words spoken by a user that are unrecognized by the speech recognition system. A speech recognition system operating in accordance with the present invention generates a hypothesis and assigns a confidence score to check if a hypothetical word corresponds to the spoken word with a high degree of probability. The speech recognition system can seek confirmation from a speaker to make sure the system reproduced the correct word. For example, if the speaker spoke the command “Call Moscow” and the word “Moscow” is not in any vocabulary or grammar of the speech recognition system, the speech recognition system would reproduce the sound sequence “moss cow” and compute a confidence score for the combination of syllables. This aspect of the present invention operates based on the assumption that it is possible to understand what a user spoke by identifying sequences of syllables. In order for the speech recognition system to implement this aspect of the present invention, the system incorporates a library that includes possible phones or syllables that might occur in a user's active vocabulary. In addition, the system includes decoding graphs indicating how individual phones or syllables can be combined.
  • In a typical implementation, this second aspect of the present invention would operate in combination with the first aspect. For example, in many instances, it would not be necessary for the system to operate with phone or syllable decoding enabled at all times, since the user would be speaking words that are recognized at least by phonetic vocabularies of the speech recognition system. However, when an utterance is encountered which is not recognized by any vocabulary or grammar of the speech recognition system, the phone/syllable decoder of the present invention would be enabled to assist in decoding of the utterance.
  • Various embodiments of the invention operate to improve the efficiency of a speech recognition system in identifying new words based on phonetic methods. For example, in one embodiment a database of viable phone/syllable sequences and associated combination probabilities is implemented to assist the speech recognition system in proposing word or utterance hypotheses with a high degree of confidence. The combination probabilities may reflect the likelihood of a two-phone or -syllable sequence, a three-phone or -syllable sequence, etc. The viable phone/syllable sequence database can be implemented in many ways in accordance with the present invention. For example, the viable phone/syllable sequence database can reflect phone/syllable sequences likely to be encountered in interactions with a particular user of a speech recognition system; phone/syllable sequences likely to be encountered with respect to a set of commands used in combination with a voice-activated user interface; phone/syllable sequences likely to be encountered in proper names and surnames; phone/syllable sequences likely to be encountered in a specific language; and phone/syllable sequences likely to be encountered in a subset of languages or all languages.
  • In further embodiments of the invention additional information—such as, for example speech and body movement biometric information—are used to identify new words. Apparatus associated with the speech recognition system detect changes in speech cadence which may be indicative of a new word. Additional apparatus associated with the speech recognition system analyze video data to detect gestures and body movements that may be indicative of introduction of a new word in the speech of a user of a speech recognition system.
  • FIG. 1 is a block diagram showing a plurality of systems that selectively may be incorporated in various embodiments of the present invention. The central system is an hierarchical mapping system 100 that receives inputs from a plurality of interconnected systems comprising the speech recognition system 10 of the present invention. The hierarchical mapping system 100 processes information received from other blocks and maps user input (such as, for example, a voice utterance) into a vocabulary subset in a hierarchical open set 105 of vocabularies. In an example, the hierarchical mapping process 100 can decode an acoustic utterance “China” into a word “China” that may belong to one of the system vocabularies but which is not recognized by a grammar set associated with a context in which the word “China” appeared. In the speech recognition system of the present invention, the hierarchical mapping process 100 adds “China” to the grammar set associated with the context in which the word “China” appeared and interprets the utterance in accordance (via semantic/context interpreter 120) with the context otherwise indicated by the utterance. A particular advantage of the present invention results from the fact that open hierarchical set 105 is comprised of open subsets (grammars and vocabularies)—as a result, these subsets are dynamic and can be updated with new words and grammatic constructs in various embodiments of the present invention. Learning module 103 is operable to learn user behavior associated with user requests (using internet facilities to learn across a plurality of users) and associate commands to user requests. In one example, a previously unrecognized command like “Call China” would be associated with an action to call a specific telephone number after the speech recognition system learns the word “China” and through interaction with a user learns to associate the command “Call China” with the action to call a specific telephone number.
  • Confidence score metrics system 104 resolves conflicts between different words and their membership in different subsets in the hierarchy. For example, referring back to the “Call China” example, there may be a word incorporated in a grammar which has a higher language model score than “China” but which has a lower acoustic score than “China”. The confidence score metrics system 104 operates in such a way to resolve these conflicts. In various embodiments of the invention, confidence scores can be assigned for acoustic models, language models and for semantic models. In embodiments of the present invention an acoustic score is assigned for a sequence of phones or syllables via phone/syllable mapper 102. The acoustic representation determined with a high degree of confidence from this scoring process may not correspond to any existing word in a set of vocabularies 106, 107, 108 or 109. In such a situation, if the confidence score block 104 evaluates the confidence metric for a new phone/syllable sequence as higher than the score for competitive words—the new sequence of phones/syllables will be considered as a new word that should be added to an open vocabulary (e.g., to 109). A meaning for the new word/phrase is received through one or both of user actions learning module 103 and semantics/context interpreter block 120. New commands are also added to a grammar 106 in embodiments of the present invention. Language model services block 107 provides language data for sequences: phones, syllables, words and phrases. This data can be used by the confidence score block 104 to derive confidence scores. This language data also can be used to compute language model scores in a decoding process operating within the hierarchical mapping system 100. User behavior biometric detector 101 provides biometrics data about user behavior (e.g., conversational biometrics) that helps to identify whether the acoustic utterance points to a new word (e.g., hesitation on some phrases, pauses, speaking stress etc.).
  • FIG. 2 is a block diagram depicting in greater detail the hierarchical mapping system 100 of FIG. 1. The hierarchical mapping system 100 contains a communications bus 200 through which different system modules exchange data. Data that enters hierarchical map system 100 (through bus 200) comprises data produced by modules previously described with respect to FIG. 1 and which are connected to 100 (e.g., speech input from 110, phonetic data in 203 from 102, confidence data in 203 from 104, biometrics data in 203 from 101 etc.).
  • Speech input 201 is directed to the hierarchical speech recognition system 202. This speech system operates to provide hierarchical decoding of, for example, phones, syllables, words and phrases. Hierarchical speech recognition system 202 also produces data for computation of hierarchical scores in 204.
  • Hierarchical score calculator 204 also uses conventional biometrics information from user biometric detector 101. For example, if the user hesitates on some acoustic utterance—a score is added to the confidence score for acoustic information (for example as linear weighted sum). For example, duration of hesitation or stress value of sounds may be normalized and added as a weighted sum. Similarly other scores (semantic, language models etc) are added as a weighted sum in more complex implementations. The confidence score is computed either for separate words, for phonetic/syllable sequences, or for membership in some subset (a grammar, vocabulary etc.) in 205. If a novel sequence of phones/syllables/phrases is chosen (via the highest confidence score) it is added by the vocabulary extender 206 to the appropriate subset.
  • FIG. 3 depicts a phone/syllable mapper 102 capable of operating in accordance with the present invention. Phonetic decoder 300 is a phonetic/syllable decoder that can be used in an hierarchical speech recognition system 202 that decodes phonetically. The phone/syllable decoder 300 uses phone 306 and syllable vocabularies 309 and phone/syllable language models 308 for decoding. Phone 306 and syllable vocabularies 309 are created from a database of names 301 (which can include names in different languages—English 302; Chinese 303; Japanese 304; Indian dialects 305). Other databases for other categories include phone classes 312 and a universal phonetic system 311 (that are applicable to several or all languages). Language models 308 and phone/syllable vocabularies 306, 309 are used to create viable phonetic/syllable sequences 310, which are derived from viable language models stored in a database or which are created dynamically. These viable sequences are not words in some vocabulary, but have a good chance to become legitimate words to be added to open vocabularies.
  • FIG. 4 depicts a user behavioral biometrics detector 101. User behavioral biometrics detector 101 comprises a speech input 400 and a video input 401. Pause detector 402 operates to detect pauses in speech; stress volume indicator 403 operates to detect stresses during speech; and speech speed measure detector 404 operates to detect changes in speech speed. Speech biometrics interpreter 408 combines information derived from the speech data by 402, 403 and 404.
  • Video data received at input 401 is operated on by head position detector 405, body movement detector 406, and gesture detector 407. Head position detector 405 that helps to identify whether a user requested some actions from a system by looking at a device—for example, by looking at a window in a car and asking to open the window. Information derived by 405, 406 and 407 are combined by body movements/gesture interpreter 409 to provide a complete biometrics picture based on user movement
  • FIG. 5 is a flow chart depicting a method of the present invention. At step 500, a speech recognition system capable of practicing the methods of the present invention receives an utterance and process the utterance. Then, at step 501, the speech recognition system decodes the acoustic data. Step 502 is a decision point where the speech recognition system decides whether the entire acoustic utterance has been decoded. If it has, then at step 503 the speech recognition system interprets the acoustic data. At step 506, the speech recognition system reaches another decision point. At step 506, the speech recognition system decides whether the entire utterance has been interpreted. If so, at step 507, a command contained in the utterance is executed.
  • Returning to step 502, if the entire acoustic utterance cannot be decoded, the speech recognition system decides whether the utterance can be decoded in an extended system. If so, it continues to step 506. If the entire utterance cannot be decoded in the extended system, the system continues to step 505 which is another decision point. At step 505, the speech recognition system determines whether there is additional biometric/context data available that points to a new word. If so, the speech recognition systems continues to step 520, where user biometric data is interpreted either implicitly or by asking questions. Then at step 509 the vocabulary is updated. If not, the utterance us interpreted by interacting with the user.
  • One of ordinary skill in the art will understand that the methods depicted and described herein can be embodied in a tangible machine-readable memory medium. A computer program fixed in a machine readable memory medium and embodying a method or methods of the present invention performs steps of the method or methods when executed by a digital processing apparatus coupled to the machine-readable memory medium. Tangible machine-readable memory media include, but are not limited to, hard drives, CD- or DVD-ROM, flash memory storage devices or in a RAM memory of a computer system.
  • Thus it is seen that the foregoing description has provided by way of exemplary and non-limiting examples a full and informative description of the best method and apparatus presently contemplated by the inventors for implementing a speech recognition system for identifying, and assigning meaning to, new words and utterances initially unknown to the speech recognition system. One skilled in the art will appreciate that the various embodiments described herein can be practiced individually; in combination with one or more other embodiments described herein; or in combination with speech recognition systems differing from those described herein. Further, one skilled in the art will appreciate that the present invention can be practiced by other than the described embodiments; that these described embodiments are presented for the purposes of illustration and not of limitation; and that the present invention is therefore limited only by the claims which follow.

Claims (21)

  1. 1. A signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus of a computer system to perform speech recognition operations, the speech recognition operations comprising:
    detecting at least a target word known to an acoustic vocabulary but unknown to an embedded grammar of a language model of the speech recognition system;
    assigning a language model probability to the target word;
    calculating a sum of an acoustic and language model confidence score for the target word and words already included in the embedded grammar of the language model; and
    if the sum of the acoustic and language model probability for the target word is greater than the sum of the acoustic and language model probability for the words already included in the embedded grammar, adding the target word to the language model.
  2. 2. The signal-bearing medium of claim 1 where the operations further comprise:
    after calculating the sum and prior to adding the target word to the embedded grammar of the language model, asking confirmation of the target word from a user of the speech recognition system; and
    receiving confirmation for the target word from the user of the speech recognition system.
  3. 3. The signal-bearing medium of claim 2 wherein confirmation comprises confirmation of the spelling of the target word.
  4. 4. The signal-bearing medium of claim 2 wherein confirmation comprises confirmation of the pronunciation of the target word.
  5. 5. A signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus of a computer system to perform speech recognition operations, the speech recognition operations comprising:
    detecting an utterance having a low acoustic score within an acoustic vocabulary of the speech recognition system indicating that the utterance may correspond to an out-of-vocabulary word;
    generating at least one new word hypothesis comprised of at least one of a phone- or syllable sequence using confidence scores derived from probabilities contained in a database of viable phone and syllable sequences; and
    if the at least one new word hypothesis meets a pre-determined criterion, adding a word corresponding to the at least one new word hypothesis to the vocabulary of the speech recognition system.
  6. 6. The signal-bearing medium of claim 5 wherein the pre-determined criterion corresponds to confirmation by a user of the speech recognition system wherein the operations further comprise:
    prior to adding at least one word to the acoustic vocabulary of the speech recognition system, presenting the new word hypothesis to a user of the speech recognition system seeking confirmation that the new word hypothesis corresponds to at least one word intended by the user when the user spoke; and
    whereby the new word is added to the vocabulary of the speech recognition system only if confirmation is receiving from the user.
  7. 7. The signal-bearing medium of claim 6 wherein the utterance corresponds to a multi-word command, and wherein the operations further comprise:
    adding the command to an embedded grammar of a language model associated with the speech recognition system.
  8. 8. The signal-bearing medium of claim 7 wherein the operations further comprise:
    adding information received from a user of the speech recognition system to memory indicating at least one action to be performed when the command is detected by the speech recognition system.
  9. 9. A signal-bearing medium tangibly embodying a program of machine-readable instructions executable by a digital processing apparatus of a computer system to perform speech recognition operations in a speech recognition system, the speech recognition operations comprising:
    detecting an utterance not recognized by at least a first one of an acoustic vocabulary, embedded grammar, and viable phone/syllable sequence library of the speech recognition system;
    generating at least one hypothesis for the utterance, wherein the hypothesis is based on information derived from a second one of an acoustic vocabulary, embedded grammar and viable phone/syllable sequence library of the speech recognition system;
    calculating a confidence score for the at least one hypothesis and for members of the first one of the acoustic vocabulary, embedded grammar and viable phone/syllable sequence library of the speech recognition system;
    comparing the confidence scores calculated for the at least one hypothesis and for members of the first one of the acoustic vocabulary, embedded grammar and viable phone/syllable sequence library of the speech recognition system; and
    adding information to the first one of an acoustic vocabulary, embedded grammar and viable phone/syllable sequence corresponding to the hypothesis if a pre-determined criterion based on the comparison is met.
  10. 10. The signal-bearing medium of claim 9 wherein the utterance corresponds to a phone sequence, and wherein the first one of the acoustic vocabulary, embedded grammar and viable phone/syllable sequence library corresponds to a particular viable phone/syllable sequence library.
  11. 11. The signal-bearing medium of claim 9 wherein the utterance corresponds to a word, and wherein the first one of the acoustic vocabulary, embedded grammar and viable phone/syllable sequence library corresponds to a particular acoustic vocabulary.
  12. 12. The signal-bearing medium of claim 9 wherein the utterance corresponds to a command, and wherein the first one of the acoustic vocabulary, embedded grammar and viable phone/syllable sequence library corresponds to a particular embedded grammar.
  13. 13. The signal-bearing medium of claim 9 wherein the at least one criterion corresponds to confirmation by a user of the speech recognition system, wherein the operations further comprise:
    prior to adding information corresponding to the at least one hypothesis to the first one of the acoustic vocabulary, embedded grammar and viable phone/syllable sequence library of the speech recognition system, seeking confirmation that the hypothesis corresponds to what the user intended when the user spoke; and
    whereby the information is added only if confirmation is received from the user of the speech recognition system.
  14. 14. The signal-bearing medium of claim 9 wherein the operations further comprise:
    using biometric information to assist in identifying the utterance as unrecognized by the first one of the acoustic vocabulary, embedded grammar and viable phone/syllable sequence library of the speech recognition system.
  15. 15. The signal signal-bearing medium of claim 14 wherein the biometric information comprises speech biometric information.
  16. 16. The signal-bearing medium of claim 14 wherein the biometric information comprises data derived from video information.
  17. 17. A speech recognition system comprising:
    a speech input for receiving speech from a user of the speech recognition system;
    an open set comprised of at least one open vocabulary and at least one open embedded grammar associated with a language model implemented in the speech recognition system;
    a hierarchical mapping system for identifying utterances not recognized by at least one of the open vocabulary and open embedded grammar of the speech recognition system; for generating hypotheses for the unrecognized utterances using confidence scores based at least in part on one of viable phone/syllable sequence information, acoustic vocabulary information and grammar information; and for adding information corresponding to the hypotheses to at least one of the open vocabulary and embedded grammar of the speech recognition system if a pre-determined criterion is met; and
    a confidence score system for generating confidence scores for use by the hierarchical mapping system.
  18. 18. The speech recognition system of claim 17 further comprising:
    a user behavior biometrics detector for generating data to assist the hierarchical mapping system in identifying utterances that a user expects not to be recognized by the speech recognition system.
  19. 19. The speech recognition system of claim 17 further comprising:
    a confirmation system for providing the hypotheses corresponding to the unrecognized utterances to a user of the speech recognition system, and for receiving confirmation from the user if the hypotheses correspond to what the user intended when the user spoke the unrecognized utterances.
  20. 20. The speech recognition system of claim 17 further comprising:
    a user input system for receiving data from the user of the speech recognition system, wherein the data is associated with the information corresponding to the hypotheses added to at least one of the open acoustic vocabulary and open embedded grammar of the speech recognition system when a pre-determined criterion is met.
  21. 21. The speech recognition system of claim 17 wherein the data concerns at least one action to be performed.
US11291231 2005-11-30 2005-11-30 Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems Abandoned US20070124147A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
US11291231 US20070124147A1 (en) 2005-11-30 2005-11-30 Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US11291231 US20070124147A1 (en) 2005-11-30 2005-11-30 Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems
US12133762 US9754586B2 (en) 2005-11-30 2008-06-05 Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems

Related Child Applications (1)

Application Number Title Priority Date Filing Date
US12133762 Continuation US9754586B2 (en) 2005-11-30 2008-06-05 Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems

Publications (1)

Publication Number Publication Date
US20070124147A1 true true US20070124147A1 (en) 2007-05-31

Family

ID=38088633

Family Applications (2)

Application Number Title Priority Date Filing Date
US11291231 Abandoned US20070124147A1 (en) 2005-11-30 2005-11-30 Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems
US12133762 Active 2026-12-27 US9754586B2 (en) 2005-11-30 2008-06-05 Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems

Family Applications After (1)

Application Number Title Priority Date Filing Date
US12133762 Active 2026-12-27 US9754586B2 (en) 2005-11-30 2008-06-05 Methods and apparatus for use in speech recognition systems for identifying unknown words and for adding previously unknown words to vocabularies and grammars of speech recognition systems

Country Status (1)

Country Link
US (2) US20070124147A1 (en)

Cited By (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070265849A1 (en) * 2006-05-11 2007-11-15 General Motors Corporation Distinguishing out-of-vocabulary speech from in-vocabulary speech
US7437291B1 (en) 2007-12-13 2008-10-14 International Business Machines Corporation Using partial information to improve dialog in automatic speech recognition systems
US20090035730A1 (en) * 2005-02-28 2009-02-05 Saab Ab Method and System for Fire Simulation
US20090319265A1 (en) * 2008-06-18 2009-12-24 Andreas Wittenstein Method and system for efficient pacing of speech for transription
US20100151427A1 (en) * 2008-12-12 2010-06-17 Institute For Information Industry Adjustable hierarchical scoring method and system
US20100268535A1 (en) * 2007-12-18 2010-10-21 Takafumi Koshinaka Pronunciation variation rule extraction apparatus, pronunciation variation rule extraction method, and pronunciation variation rule extraction program
US20110069230A1 (en) * 2009-09-22 2011-03-24 Caption Colorado L.L.C. Caption and/or Metadata Synchronization for Replay of Previously or Simultaneously Recorded Live Programs
US20110093259A1 (en) * 2008-06-27 2011-04-21 Koninklijke Philips Electronics N.V. Method and device for generating vocabulary entry from acoustic data
US20110125499A1 (en) * 2009-11-24 2011-05-26 Nexidia Inc. Speech recognition
US20110184737A1 (en) * 2010-01-28 2011-07-28 Honda Motor Co., Ltd. Speech recognition apparatus, speech recognition method, and speech recognition robot
US20110288859A1 (en) * 2010-02-05 2011-11-24 Taylor Andrew E Language context sensitive command system and method
US8473293B1 (en) * 2012-04-17 2013-06-25 Google Inc. Dictionary filtering using market data
US8560310B1 (en) * 2012-05-08 2013-10-15 Nuance Communications, Inc. Method and apparatus providing improved voice activated functions
US20140025377A1 (en) * 2012-07-18 2014-01-23 International Business Machines Corporation System, method and program product for providing automatic speech recognition (asr) in a shared resource environment
US20140379338A1 (en) * 2013-06-20 2014-12-25 Qnx Software Systems Limited Conditional multipass automatic speech recognition
US20150019221A1 (en) * 2013-07-15 2015-01-15 Chunghwa Picture Tubes, Ltd. Speech recognition system and method
EP2860727A4 (en) * 2012-09-26 2015-07-01 Huawei Tech Co Ltd Voice recognition method and device
US20150221305A1 (en) * 2014-02-05 2015-08-06 Google Inc. Multiple speech locale-specific hotword classifiers for selection of a speech locale
US20150287405A1 (en) * 2012-07-18 2015-10-08 International Business Machines Corporation Dialect-specific acoustic language modeling and speech recognition
US20160240188A1 (en) * 2013-11-20 2016-08-18 Mitsubishi Electric Corporation Speech recognition device and speech recognition method
US9437189B2 (en) * 2014-05-29 2016-09-06 Google Inc. Generating language models
US9460713B1 (en) * 2015-03-30 2016-10-04 Google Inc. Language model biasing modulation

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB201306716D0 (en) * 2010-10-15 2013-05-29 Intelligent Mechatronic Sys Implicit association and polymorphism driven human machine interaction
JP6052814B2 (en) * 2014-09-24 2016-12-27 インターナショナル・ビジネス・マシーンズ・コーポレーションInternational Business Machines Corporation Method for constructing a speech recognition model, the speech recognition method, a computer system, a voice recognition device, a program and a recording medium
US9947313B2 (en) * 2015-01-26 2018-04-17 William Drewes Method for substantial ongoing cumulative voice recognition error reduction
US20160275942A1 (en) * 2015-01-26 2016-09-22 William Drewes Method for Substantial Ongoing Cumulative Voice Recognition Error Reduction
US20160322044A1 (en) * 2015-04-01 2016-11-03 Elwha Llc Networked User Command Recognition
US9740678B2 (en) 2015-06-25 2017-08-22 Intel Corporation Method and system of automatic speech recognition with dynamic vocabularies

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6219640B1 (en) * 1999-08-06 2001-04-17 International Business Machines Corporation Methods and apparatus for audio-visual speaker recognition and utterance verification
US6816836B2 (en) * 1999-08-06 2004-11-09 International Business Machines Corporation Method and apparatus for audio-visual speech detection and recognition
US6937702B1 (en) * 2002-05-28 2005-08-30 West Corporation Method, apparatus, and computer readable media for minimizing the risk of fraudulent access to call center resources
US6941264B2 (en) * 2001-08-16 2005-09-06 Sony Electronics Inc. Retraining and updating speech models for speech recognition
US7103542B2 (en) * 2001-12-14 2006-09-05 Ben Franklin Patent Holding Llc Automatically improving a voice recognition system

Family Cites Families (55)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5027406A (en) * 1988-12-06 1991-06-25 Dragon Systems, Inc. Method for interactive speech recognition and training
US5233681A (en) * 1992-04-24 1993-08-03 International Business Machines Corporation Context-dependent speech recognizer using estimated next word context
US6311157B1 (en) * 1992-12-31 2001-10-30 Apple Computer, Inc. Assigning meanings to utterances in a speech recognition system
US6064959A (en) * 1997-03-28 2000-05-16 Dragon Systems, Inc. Error correction in speech recognition
US6167377A (en) * 1997-03-28 2000-12-26 Dragon Systems, Inc. Speech recognition language models
CA2329345A1 (en) * 1997-04-22 1998-10-29 Greg Hetherington Method and apparatus for processing free-format data
US6125345A (en) * 1997-09-19 2000-09-26 At&T Corporation Method and apparatus for discriminative utterance verification using multiple confidence measures
US6154722A (en) * 1997-12-18 2000-11-28 Apple Computer, Inc. Method and apparatus for a speech recognition system language model that integrates a finite state grammar probability and an N-gram probability
US6298324B1 (en) * 1998-01-05 2001-10-02 Microsoft Corporation Speech recognition system with changing grammars and grammar help command
US6233553B1 (en) * 1998-09-04 2001-05-15 Matsushita Electric Industrial Co., Ltd. Method and system for automatically determining phonetic transcriptions associated with spelled words
US6606598B1 (en) * 1998-09-22 2003-08-12 Speechworks International, Inc. Statistical computing and reporting for interactive speech applications
CA2346145A1 (en) * 1998-10-05 2000-04-13 Lernout & Hauspie Speech Products N.V. Speech controlled computer user interface
US6839669B1 (en) * 1998-11-05 2005-01-04 Scansoft, Inc. Performing actions identified in recognized speech
US6571210B2 (en) * 1998-11-13 2003-05-27 Microsoft Corporation Confidence measure system using a near-miss pattern
US6502072B2 (en) * 1998-11-20 2002-12-31 Microsoft Corporation Two-tier noise rejection in speech recognition
US7668718B2 (en) * 2001-07-17 2010-02-23 Custom Speech Usa, Inc. Synchronized pattern recognition source data processed by manual or automatic means for creation of shared speaker-dependent speech user profile
US7120582B1 (en) * 1999-09-07 2006-10-10 Dragon Systems, Inc. Expanding an effective vocabulary of a speech recognition system
US6542866B1 (en) * 1999-09-22 2003-04-01 Microsoft Corporation Speech recognition method and apparatus utilizing multiple feature streams
JP5118280B2 (en) * 1999-10-19 2013-01-16 ソニー エレクトロニクス インク Natural language interface control system
US6421641B1 (en) * 1999-11-12 2002-07-16 International Business Machines Corporation Methods and apparatus for fast adaptation of a band-quantized speech decoding system
EP1215662A4 (en) * 2000-02-28 2005-09-21 Sony Corp Speech recognition device and speech recognition method, and recording medium
US6473734B1 (en) * 2000-03-27 2002-10-29 Motorola, Inc. Methodology for the use of verbal proxies for dynamic vocabulary additions in speech interfaces
US7200555B1 (en) * 2000-07-05 2007-04-03 International Business Machines Corporation Speech recognition correction for devices having limited or no display
US6694296B1 (en) * 2000-07-20 2004-02-17 Microsoft Corporation Method and apparatus for the recognition of spelled spoken words
US6836760B1 (en) * 2000-09-29 2004-12-28 Apple Computer, Inc. Use of semantic inference and context-free grammar with speech recognition system
US6937983B2 (en) * 2000-12-20 2005-08-30 International Business Machines Corporation Method and system for semantic speech recognition
US6973427B2 (en) * 2000-12-26 2005-12-06 Microsoft Corporation Method for adding phonetic descriptions to a speech recognition lexicon
JP2002215187A (en) * 2001-01-23 2002-07-31 Matsushita Electric Ind Co Ltd Speech recognition method and device for the same
US6976019B2 (en) * 2001-04-20 2005-12-13 Arash M Davallou Phonetic self-improving search engine
US7286985B2 (en) * 2001-07-03 2007-10-23 Apptera, Inc. Method and apparatus for preprocessing text-to-speech files in a voice XML application distribution system using industry specific, social and regional expression rules
US7920682B2 (en) * 2001-08-21 2011-04-05 Byrne William J Dynamic interactive voice interface
US7444286B2 (en) * 2001-09-05 2008-10-28 Roth Daniel L Speech recognition using re-utterance recognition
US20030120493A1 (en) * 2001-12-21 2003-06-26 Gupta Sunil K. Method and system for updating and customizing recognition vocabulary
US7167831B2 (en) * 2002-02-04 2007-01-23 Microsoft Corporation Systems and methods for managing multiple grammars in a speech recognition system
US7089188B2 (en) * 2002-03-27 2006-08-08 Hewlett-Packard Development Company, L.P. Method to expand inputs for word or document searching
JP3967952B2 (en) * 2002-04-16 2007-08-29 富士通株式会社 Grammar update system and method
CN1453767A (en) * 2002-04-26 2003-11-05 日本先锋公司 Speech recognition apparatus and speech recognition method
US7587318B2 (en) * 2002-09-12 2009-09-08 Broadcom Corporation Correlating video images of lip movements with audio signals to improve speech recognition
US7031915B2 (en) * 2003-01-23 2006-04-18 Aurilab Llc Assisted speech recognition by dual search acceleration technique
WO2004075168A1 (en) * 2003-02-19 2004-09-02 Matsushita Electric Industrial Co., Ltd. Speech recognition device and speech recognition method
US7720683B1 (en) * 2003-06-13 2010-05-18 Sensory, Inc. Method and apparatus of specifying and performing speech recognition operations
US7343289B2 (en) * 2003-06-25 2008-03-11 Microsoft Corp. System and method for audio/video speaker detection
US20050091036A1 (en) * 2003-10-23 2005-04-28 Hazel Shackleton Method and apparatus for a hierarchical object model-based constrained language interpreter-parser
US7590533B2 (en) * 2004-03-10 2009-09-15 Microsoft Corporation New-word pronunciation learning using a pronunciation graph
EP1723636A1 (en) * 2004-03-12 2006-11-22 Siemens Aktiengesellschaft User and vocabulary-adaptive determination of confidence and rejecting thresholds
ES2311872T3 (en) * 2004-12-28 2009-02-16 Loquendo S.P.A. System and method of automatic speech recognition.
US7680659B2 (en) * 2005-06-01 2010-03-16 Microsoft Corporation Discriminative training for language modeling
US8090584B2 (en) * 2005-06-16 2012-01-03 Nuance Communications, Inc. Modifying a grammar of a hierarchical multimodal menu in dependence upon speech command frequency
US8396715B2 (en) * 2005-06-28 2013-03-12 Microsoft Corporation Confidence threshold tuning
US7826945B2 (en) * 2005-07-01 2010-11-02 You Zhang Automobile speech-recognition interface
US7640160B2 (en) * 2005-08-05 2009-12-29 Voicebox Technologies, Inc. Systems and methods for responding to natural language speech utterance
US7620549B2 (en) * 2005-08-10 2009-11-17 Voicebox Technologies, Inc. System and method of supporting adaptive misrecognition in conversational speech
US7542904B2 (en) * 2005-08-19 2009-06-02 Cisco Technology, Inc. System and method for maintaining a speech-recognition grammar
EP1934971A4 (en) * 2005-08-31 2010-10-27 Voicebox Technologies Inc Dynamic speech sharpening
US7689420B2 (en) * 2006-04-06 2010-03-30 Microsoft Corporation Personalizing a context-free grammar using a dictation language model

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6219640B1 (en) * 1999-08-06 2001-04-17 International Business Machines Corporation Methods and apparatus for audio-visual speaker recognition and utterance verification
US6816836B2 (en) * 1999-08-06 2004-11-09 International Business Machines Corporation Method and apparatus for audio-visual speech detection and recognition
US6941264B2 (en) * 2001-08-16 2005-09-06 Sony Electronics Inc. Retraining and updating speech models for speech recognition
US7103542B2 (en) * 2001-12-14 2006-09-05 Ben Franklin Patent Holding Llc Automatically improving a voice recognition system
US6937702B1 (en) * 2002-05-28 2005-08-30 West Corporation Method, apparatus, and computer readable media for minimizing the risk of fraudulent access to call center resources

Cited By (41)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090035730A1 (en) * 2005-02-28 2009-02-05 Saab Ab Method and System for Fire Simulation
US8688451B2 (en) * 2006-05-11 2014-04-01 General Motors Llc Distinguishing out-of-vocabulary speech from in-vocabulary speech
US20070265849A1 (en) * 2006-05-11 2007-11-15 General Motors Corporation Distinguishing out-of-vocabulary speech from in-vocabulary speech
US7437291B1 (en) 2007-12-13 2008-10-14 International Business Machines Corporation Using partial information to improve dialog in automatic speech recognition systems
US20090157405A1 (en) * 2007-12-13 2009-06-18 International Business Machines Corporation Using partial information to improve dialog in automatic speech recognition systems
US7624014B2 (en) 2007-12-13 2009-11-24 Nuance Communications, Inc. Using partial information to improve dialog in automatic speech recognition systems
US8595004B2 (en) * 2007-12-18 2013-11-26 Nec Corporation Pronunciation variation rule extraction apparatus, pronunciation variation rule extraction method, and pronunciation variation rule extraction program
US20100268535A1 (en) * 2007-12-18 2010-10-21 Takafumi Koshinaka Pronunciation variation rule extraction apparatus, pronunciation variation rule extraction method, and pronunciation variation rule extraction program
US8332212B2 (en) * 2008-06-18 2012-12-11 Cogi, Inc. Method and system for efficient pacing of speech for transcription
US20090319265A1 (en) * 2008-06-18 2009-12-24 Andreas Wittenstein Method and system for efficient pacing of speech for transription
US20110093259A1 (en) * 2008-06-27 2011-04-21 Koninklijke Philips Electronics N.V. Method and device for generating vocabulary entry from acoustic data
US8751230B2 (en) * 2008-06-27 2014-06-10 Koninklijke Philips N.V. Method and device for generating vocabulary entry from acoustic data
US20100151427A1 (en) * 2008-12-12 2010-06-17 Institute For Information Industry Adjustable hierarchical scoring method and system
US8157566B2 (en) * 2008-12-12 2012-04-17 Institute For Information Industry Adjustable hierarchical scoring method and system
US20110069230A1 (en) * 2009-09-22 2011-03-24 Caption Colorado L.L.C. Caption and/or Metadata Synchronization for Replay of Previously or Simultaneously Recorded Live Programs
US10034028B2 (en) 2009-09-22 2018-07-24 Vitac Corporation Caption and/or metadata synchronization for replay of previously or simultaneously recorded live programs
US8707381B2 (en) * 2009-09-22 2014-04-22 Caption Colorado L.L.C. Caption and/or metadata synchronization for replay of previously or simultaneously recorded live programs
US9275640B2 (en) * 2009-11-24 2016-03-01 Nexidia Inc. Augmented characterization for speech recognition
US20110125499A1 (en) * 2009-11-24 2011-05-26 Nexidia Inc. Speech recognition
US8886534B2 (en) * 2010-01-28 2014-11-11 Honda Motor Co., Ltd. Speech recognition apparatus, speech recognition method, and speech recognition robot
US20110184737A1 (en) * 2010-01-28 2011-07-28 Honda Motor Co., Ltd. Speech recognition apparatus, speech recognition method, and speech recognition robot
US20110288859A1 (en) * 2010-02-05 2011-11-24 Taylor Andrew E Language context sensitive command system and method
US8473293B1 (en) * 2012-04-17 2013-06-25 Google Inc. Dictionary filtering using market data
US8560310B1 (en) * 2012-05-08 2013-10-15 Nuance Communications, Inc. Method and apparatus providing improved voice activated functions
US20140025377A1 (en) * 2012-07-18 2014-01-23 International Business Machines Corporation System, method and program product for providing automatic speech recognition (asr) in a shared resource environment
US9966064B2 (en) * 2012-07-18 2018-05-08 International Business Machines Corporation Dialect-specific acoustic language modeling and speech recognition
US9043208B2 (en) * 2012-07-18 2015-05-26 International Business Machines Corporation System, method and program product for providing automatic speech recognition (ASR) in a shared resource environment
US9053708B2 (en) * 2012-07-18 2015-06-09 International Business Machines Corporation System, method and program product for providing automatic speech recognition (ASR) in a shared resource environment
US20140025380A1 (en) * 2012-07-18 2014-01-23 International Business Machines Corporation System, method and program product for providing automatic speech recognition (asr) in a shared resource environment
US20150287405A1 (en) * 2012-07-18 2015-10-08 International Business Machines Corporation Dialect-specific acoustic language modeling and speech recognition
EP2860727A4 (en) * 2012-09-26 2015-07-01 Huawei Tech Co Ltd Voice recognition method and device
US9368108B2 (en) 2012-09-26 2016-06-14 Huawei Technologies Co., Ltd. Speech recognition method and device
US20140379338A1 (en) * 2013-06-20 2014-12-25 Qnx Software Systems Limited Conditional multipass automatic speech recognition
US20150019221A1 (en) * 2013-07-15 2015-01-15 Chunghwa Picture Tubes, Ltd. Speech recognition system and method
US9711136B2 (en) * 2013-11-20 2017-07-18 Mitsubishi Electric Corporation Speech recognition device and speech recognition method
US20160240188A1 (en) * 2013-11-20 2016-08-18 Mitsubishi Electric Corporation Speech recognition device and speech recognition method
US9589564B2 (en) * 2014-02-05 2017-03-07 Google Inc. Multiple speech locale-specific hotword classifiers for selection of a speech locale
US20150221305A1 (en) * 2014-02-05 2015-08-06 Google Inc. Multiple speech locale-specific hotword classifiers for selection of a speech locale
US9437189B2 (en) * 2014-05-29 2016-09-06 Google Inc. Generating language models
US9460713B1 (en) * 2015-03-30 2016-10-04 Google Inc. Language model biasing modulation
US9886946B2 (en) 2015-03-30 2018-02-06 Google Llc Language model biasing modulation

Also Published As

Publication number Publication date Type
US20080270136A1 (en) 2008-10-30 application
US9754586B2 (en) 2017-09-05 grant

Similar Documents

Publication Publication Date Title
Furui 50 years of progress in speech and speaker recognition research
US6681206B1 (en) Method for generating morphemes
US6973427B2 (en) Method for adding phonetic descriptions to a speech recognition lexicon
US6910012B2 (en) Method and system for speech recognition using phonetically similar word alternatives
US6601027B1 (en) Position manipulation in speech recognition
US5787230A (en) System and method of intelligent Mandarin speech input for Chinese computers
US5027406A (en) Method for interactive speech recognition and training
Juang et al. Automatic recognition and understanding of spoken language-a first step toward natural human-machine communication
US7085716B1 (en) Speech recognition using word-in-phrase command
US7228275B1 (en) Speech recognition system having multiple speech recognizers
US5873061A (en) Method for constructing a model of a new word for addition to a word model database of a speech recognition system
US6694296B1 (en) Method and apparatus for the recognition of spelled spoken words
US7013275B2 (en) Method and apparatus for providing a dynamic speech-driven control and remote service access system
US8073681B2 (en) System and method for a cooperative conversational voice user interface
US20020123894A1 (en) Processing speech recognition errors in an embedded speech recognition system
US6587818B2 (en) System and method for resolving decoding ambiguity via dialog
US20080177541A1 (en) Voice recognition device, voice recognition method, and voice recognition program
US6839667B2 (en) Method of speech recognition by presenting N-best word candidates
US6067514A (en) Method for automatically punctuating a speech utterance in a continuous speech recognition system
US8285546B2 (en) Method and system for identifying and correcting accent-induced speech recognition difficulties
US5855000A (en) Method and apparatus for correcting and repairing machine-transcribed input using independent or cross-modal secondary input
US20060009965A1 (en) Method and apparatus for distribution-based language model adaptation
Juang et al. Automatic speech recognition–a brief history of the technology development
US20070100635A1 (en) Combined speech and alternate input modality to a mobile device
US20080059186A1 (en) Intelligent speech recognition of incomplete phrases

Legal Events

Date Code Title Description
AS Assignment

Owner name: INTERNATIONAL BUSINESS MACHINES CORPORATION, NEW Y

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:DELIGNE, SABINE;GOPINATH, RAMESH A.;KANEVSKY, DIMITRI;AND OTHERS;REEL/FRAME:017520/0139;SIGNING DATES FROM 20060301 TO 20060407

AS Assignment

Owner name: NUANCE COMMUNICATIONS, INC., MASSACHUSETTS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:INTERNATIONAL BUSINESS MACHINES CORPORATION;REEL/FRAME:022689/0317

Effective date: 20090331

Owner name: NUANCE COMMUNICATIONS, INC.,MASSACHUSETTS

Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:INTERNATIONAL BUSINESS MACHINES CORPORATION;REEL/FRAME:022689/0317

Effective date: 20090331