US20150066506A1 - System and Method of Text Zoning - Google Patents
System and Method of Text Zoning Download PDFInfo
- Publication number
- US20150066506A1 US20150066506A1 US14/467,783 US201414467783A US2015066506A1 US 20150066506 A1 US20150066506 A1 US 20150066506A1 US 201414467783 A US201414467783 A US 201414467783A US 2015066506 A1 US2015066506 A1 US 2015066506A1
- Authority
- US
- United States
- Prior art keywords
- utterances
- audio data
- meaning
- transcription
- 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
Links
Images
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
- G10L15/1822—Parsing for meaning understanding
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/26—Speech to text systems
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/04—Segmentation; Word boundary detection
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/08—Speech classification or search
- G10L15/18—Speech classification or search using natural language modelling
Abstract
A method of zoning a transcription of audio data includes separating the transcription of audio data into a plurality of utterances. A that each word in an utterances is a meaning unit boundary is calculated. The utterance is split into two new utterances at a work with a maximum calculated probability. At least one of the two new utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.
Description
- The present application claims priority of U.S. Provisional Patent Application No. 61/872,224, filed on Aug. 30, 2013, the content of which is hereby incorporated herein by reference in its entirety.
- In the case of automated transcription of audio data often the output transcription in likely in the form of a single text file. In many instances, the audio data in fact contains a conversation between multiple speakers. Even within a case of a single speaker, automated transcriptions will be devoid of punctuation. These natural events provide context for analysis of the content of the transcription. Without such natural cues to provide context, meaningful analysis may be difficult and inaccurate. Therefore it is desirable for an automated system and method automatedly zone or segment the transcription file.
- A method of zoning a transcription of auto data includes separating the transcription of auto data into a plurality of utterances. Utterances of the plurality of utterances that are shorter then a predetermined minimum threshold are identified as meaning units. A probability is calculated that each word in an utterance of the plurality of utterances which are longer than the predetermined minimum threshold is a meaning unit boundary. The utterance of the plurality of utterances which is longer than the predetermined minimum threshold is split into two new utterances at a word with a maximum calculated probability. At least one of the two utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.
- In an additional exemplary embodiment of a method of zoning a transcription of auto data, the transcription of auto data is separated into a plurality of utterances. Utterances of the plurality that are shorter than a predetermined minimum threshold are identified as meaning units. Utterances of the plurality of utterances that are longer than the predetermined minimum threshold are selected for subdivision. The selected utterances are split into windows. Each window is twice a maximum utterance threshold. A probability that each word in the plurality of windows is a meaning unit boundary is calculated based upon at least a linguistic model applied to each of the plurality of windows. The selected utterances which are longer than the predetermined minimum threshold are split into two new utterances at a word with a maximum calculated probability. At least one of the two new utterances that is shorter than a maximum utterance threshold is identified as a meaning unit.
-
FIG. 1 is a flow chart that depicts an exemplary embodiment of a method of textual zoning. -
FIG. 2 is a system diagram of an exemplary embodiment of a system for performing textual zoning. -
FIG. 3 is a flow chart that depicts an exemplary embodiment of a method of analytics of a textually zoned transcription. - Meaning units are effectively the building blocks of a specific speech, interaction, or disclosure. A meaning unit can be considered to be a realization of an illocutionary force (intent), a conceptual content, a syntactic dependency (grammatical relation), and a prosodic contour. Zoning of a transcript as disclosed herein, seeks to find optimal positions of boundaries between meaning units within the transcript. In an embodiment, the transcript is an automated transcription of audio data. Embodiments as disclosed herein have been found to be more accurate in automatedly zoning transcripts. More accurate identification of meaning units both makes later analysis and analytics of the meaning units less computationally demanding and produces more accurate and meaningful results because appropriate context is available more frequently.
- Referring first to
FIG. 3 ,FIG. 3 is a flow chart that depicts an exemplary embodiment of amethod 300 of audio data analysis. Themethod 300 begins with audio data that is obtained at 302. The audio data at 302 may exemplarily be a .WAV tile, but may include a variety of other types of audio files. Additionally, the audio data obtained at 302 is exemplarily a mono audio file; however, it is to be recognized that in other embodiments, the audio data may be stereo audio. In still further embodiments, the audio data may be streaming audio data received in real-time or near real-time. Themethod 300 may also be implemented in embodiments where the audio data obtained at 302 is previously recorded and stored. The audio data may be initially processed in order to segment the audio data into a plurality of overlapping segments. In a non-limiting example, the audio data may be segmented into 20-25 ms segments taken every 10 ms. - The segmented audio data undergoes a decoding process at 304 in order to produce a transcription at 306. In an exemplary embodiment, the decoding process at 304 is a large vocabulary continuous speech recognition (LVCSR) decoding. In a non-limiting embodiment, the LVCSR may be performed using the Viterbi algorithm to apply one or more models to the audio data in order to arrive at the best or most likely transcription of the audio data. In a non-limiting embodiment, the Viterbi algorithm applies at least a
linguistic model 308 in the decoding process at 304. - A linguistic model such as the one used at 308 is exemplarily a dictionary of words combined with statistics on the frequency of occurrences of the words in the dictionary as well as statistics on the frequency of the words in the dictionary in relation to other adjacent words. More specifically, the linguistic model may provide statistics, distributions, and/or frequencies of specific word pairs or word triplets. While a generic linguistic model may simply be based upon generalized each patterns and word occurrences, linguistic models can be much more effective when contextual assumptions are made that match the content of the audio data to be transcribed. Therefore, linguistic models can be more effective at decoding specialized audio data when the models are specifically developed to transcribe audio data with technical or specific vocabularies, e.g. medical or legal audio data, Linguistic models can also include scripts or other known sequences of words that are commonly occurring the context of the obtained audio data. Models may also be produced using automated of machine learning techniques.
- The transcription produced at 306 is exemplarily a text file of the best or most probable sequence of words based upon the application of the percentages and statistics of the linguistic model to the audio data in the
decoding process 304. At 310 text file of thetranscription 306 is zoned into segments or meaning units as will be described in further detail herein. In an embodiment, the zoning at 310 applies both anacoustic model 312 and alinguistic model 314. In one embodiment, the linguistic model applied at 314 is the same linguistic model as applied at 308 in the decoding process at 304. However, in other embodiments, an alternative linguistic model is used at 314. - The output of the zoning at 310 is a sequence of meaning units at 316. In a merely exemplary embodiment, a meaning unit may be a segment of twenty or fewer words that are likely to be spoken by the same speaker and contextually related. These meaning units at 316 are well-suited for the application of speech analytics at 318. In the speech analytics at 318, a variety of analyses may be performed in order to identify context, content, or other information from the transcribed audio data. In embodiments as disclosed in further detail herein, the meaning units segmented by the zoning process at 310 strike a desirable balance while avoiding too long the of phrases which make the identification of repetitive patterns difficult, while also avoiding too short of phrases which may not provide enough context, content, or meaning for effective analytics.
-
FIG. 1 is a flow chart that depicts an exemplary embodiment of amethod 100 of textual zoning.FIG. 2 is a system diagram of an exemplary embodiment of asystem 200 for textual zoning. Thesystem 200 is generally a computing system that includes aprocessing system 206,storage system 204,software 202,communication interface 208 and a user interface 210, Theprocessing system 206 loads and executessoftware 202 from thestorage system 204, including asoftware module 230. When executed by thecomputing system 200,software module 230 directs theprocessing system 206 to operate as described herein in further detail in accordance with themethod 100. It is to be recognized that in embodiments, thecomputing system 200 may also carry out some or all of themethod 300. - Although the
computing system 200 as depicted inFIG. 2 includes one software module in the present example, it should be understood that one or more modules could provide the same operation. Similarly, while the description as provided herein refers to acomputing system 200 and aprocessing system 206, it is to be recognized that implementations of such systems can be performed using one or more processors, which may be communicatively connected, and such implementations are considered to be within the scope of the description. - The
processing system 206 can comprise a microprocessor and other circuitry that retrieves and executessoftware 202 fromstorage system 204. Theprocessing system 206 can be implemented within a single processing device but can also be distributed across multiple processing devices or sub-systems that cooperate in executing program instructions. Examples ofprocessing system 206 include general-purpose central processing units, application specific processors, and logic devices, as well as any other type of processing device, combinations of processing devices, or variations thereof. - The
storage system 204 can comprise any storage medium readable byprocessing system 206, and capable of storingsoftware 202. Thestorage system 204 can include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data.Storage system 204 can be implemented as a single storage device but may also be implemented across multiple storage devices or subsystems.Storage system 204 can further include additional elements, such as a controller capable of communicating with theprocessing system 206. - Examples of storage media include a random access memory, read only memory, magnetic disks, optical disks, flash memory, virtual memory, and non-virtual memory, magnetic sets, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and that may be accessed by an instruction execution system, as well as any combination or variation thereof, or any other type of storage medium.
- User interface 210 can include a mouse, a keyboard, a voice input device, a touch input device for receiving a gesture from a user, a motion input device for detecting non-touch gestures and other motions by a user, and other comparable input devices and associated processing elements capable of receiving user input from a user. Output devices such as a video display or graphical display can display and interface further associated with embodiments of the system and method as disclosed herein. The speakers, printers, haptic devices, and other types of output devices may also be included, in the user interface 210.
- As described in further detail herein, the
computing system 200 receivesaudio data 220 at thecommunication interface 208. In embodiments, thecommunication interface 208 operates to send and/or receive data from other devices to which thecomputing system 200 is communicatively connected. Theaudio data 220 may be an audio recording or a conversation, which ma exemplarily be between two speakers, although the audio recording may be any of a variety of other audio records, including multiple speakers, a single speaker, or an automated or recorded auditory message. The audio data may exemplarily be a .wav format, but may also be other types of audio formats, exemplarily in a pulse code modulated (PCM) format and a further example may include linear pulse code modulated (LPCM) audio data. Furthermore, the audio data is exemplarily mono audio; however, it is recognized that embodiments of the method as disclosed herein may also be used with stereo audio data. In still further embodiments, the audio data may be streaming audio data received in real-time or near real-time by thecomputing system 200. In an exemplary embodiment as reference herein the audio data may be of a customer service interaction, exemplarily between a customer service agent and a customer although it will be recognized that embodiment as disclosed herein may be used in other functions and contexts. -
FIG. 1 is a flow chart that depicts an exemplary embodiment of amethod 100 of textual zoning. Themethod 100 begins at 102 with a transcription such as described above with respect toFIG. 3 . In an exemplary embodiment, thetranscription 102 is obtained by an LVCSR transcription of audio data. - In examples as disclosed herein, utterances are consecutive sequences of words spoken by one speaker in a conversation without interference by another speaker or another event. Meaning units divide utterances into a basic segment of meaning or the equivalent of a sentence, when narrated text is compared to written text. A meaning unit may be a sequence of words spoken by one speaker in a conversation without interference. In some embodiments, the meaning unit may include some level of speaker interference, e.g. very short acknowledgement statements by the other speaker. All terms in the meaning unit are linked within the boundaries of the meaning unit. A call segment is a set of utterances within a call, usually consecutive utterances, that are related to a specific topic. Non-limiting examples of call segments may include, call beginning, customer detail verification, call reason, problem description, problem resolution, and call finalization. A dialog act is related to a call segment in that the dialog act indicates some intention of the speaker or denotes a function or purpose of an utterance or meeting unit within the customer service interaction. Non-limiting examples of dialog acts may include an identification whether a utterance/meaning unit is a statement, question, greeting, or such. In some embodiments, a category or a user defined labeled predicate is assigned to the data file exemplarily by the customer service agent to identify a domain of the customer service interaction. In an alternative embodiment, the category may be determined through the application of rules that are expressed as a query language using, logical operators on variables (text) extracted from the call.
- At 104, the transcription undergoes a speaker separation in which the transcription is divided into utterances. An utterance is a relatively short grouping of words that have a high probability to have been spoken by the same speaker. In one embodiment, the speaker separation at 104 may be performed by applying an acoustic model to the audio data processed to obtain the transcription. Alternatively, the entropy of the audio data can be evaluated to separate speakers. These analyses can identify pauses or breaks in the audio data that may correspond a change between speakers. In an alternative embodiment the speaker separation can be achieved by applying a linguistic model to the transcription. The application of the linguistic, model to the transcription can use probabilities found in the linguistic model that identify when groupings of words are likely to have emanated from the same speaker. In a still further embodiment a combination of acoustic, entropal, and linguistic, analysis is used to achieve speaker separation. In a still further embodiment, if the audio data is of an interaction between two or more people and at least one of the speakers in the conversation is known, then the identified utterances can be attributed to one of the speakers of the known two or more speakers in a process known as diarization.
- At 106, each of the utterances is evaluated based upon length. If an utterance is shorter than a predetermined threshold number of words, then the utterance is identified at 108 as a meaning unit. In an embodiment, the predetermined threshold is two times a minimum utterance length for splitting into two meaning units. In an exemplary embodiment, the minimum utterance length for splitting into meaning units may be fifteen words and therefore the predetermined threshold number of words is thirty words. Therefore, at 106 if the utterance is less than thirty words, then this utterance is identified at 108 as a meaning unit. If the utterance is longer than the predetermined threshold number of words, then the utterance as a whole is deemed to contain more than one meaning unit and therefore the utterance must be optimally subdivided as disclosed herein in order to automatedly extract the meaning units from the utterance.
- The utterances that are determined at 106 to be longer than the predetermined threshold number of words may be further split into windows at 110. Since words that are fin apart tend to be less correlated, an utterance may be split into a window of a predetermined word length. In an exemplary embodiment, the predetermined word length may be two times a predetermined maximum utterance size. In such an embodiment, the window is therefore ensured not to require that more than two utterances must be identified within the window, although in accordance with the processing, disclosed herein a window may be eventually divided in to two or more utterances. In a merely exemplary embodiment, the maximum utterance size may be twenty words. The splitting of the along utterance into windows serves the purpose of simplifying the calculation and analysis, and also helps to obtain more accurate identification of meaning unit boundaries within the long utterance.
- At 112 for each word in a window, a percentage or probability that that word is a meaning unit boundary is calculated. This can be performed by applying a linguistic model to the transcription. In an embodiment, the linguistic model is an N-gram linguistic model which contains the probability of words to appear before and/or after words MO or markers of the beginning or the end of a meaning unit (<s>, </s>). In an exemplary embodiment, the linguistic model applied at 112 may be the same linguistic model as used to obtain the transcription at 102 of the audio data. It will also be recognized that the linguistic models used at 112 and 102 may also be different models in an alternative embodiment.
- In the linguistic model, the model may provide linguistic, statistics and probabilities that particular words signal or indicate a change of a speaker or a change in a meaning unit. As an example, “wh” question words (e.g. who, what, where, why, when) tend to start the beginning of a sentence which is likely to either be the beginning of a new speaker or indicate the start of a new meaning unit. Other non-limiting examples of such meaning unit boundary words may include “so” or “and.”
- In addition to including probabilities related to individual words as indicating the start of a new meaning unit, the linguistic model may also provide probabilities as to the likelihood that short combinations of words. These probabilities may exemplarily include probabilities that word doublets or triplets are spoken together within a single meaning unit as opposed to emanating from separate speakers or separate meaning units. In a non-limiting example, short phrases such as “now what” or “guess what” include a “wh” question word as described above as having a high likelihood of indicating a new meaning unit; however, the statistics in the linguistic model may show that when the word “what” is found in a doublet of either of these combinations, then the “what” in either of these doublets does not start a new meaning unit.
- For each window with the words W1 . . . WN a probability is calculated that the phrase has no meaning unit boundary: P(baseline)=P(W1 . . . WN)/N. Additionally, the probability that a meaning unit boundary appears after each word (Wk) in the window is calculated as follows:
-
- At 114, the window is split into new utterances at the word determined to have the maximum probability that the word is the meaning unit boundary as calculated above. This determination is, however, subject to one or more exceptions or exclusions which may exemplarily be defined as linguistic exceptions at 116 or acoustic exceptions at 118. The following are exemplary linguistic exceptions as may be used in an embodiment of the method as disclosed herein:
-
P(k is MU boundary)>MIN_BOUNDARY_PROBABILITY -
P(k is MU boundary)−P(baseline)>MIN_PROBABILITY_DIFF - An example of an
acoustic exception 118 may be the identification of long pauses in speech. Such an acoustical analysis may require analysis of the audio data that was processed to result in the original transcription at 102. For example, a break of longer than 200 ms in the audio data may be an independent cue, apart from the linguistic and textual analysis as described above indicative of a new meaning unit. Long pauses in speech may exemplarily reflect the speaker taking punctuation which can likely form a meaning unit boundary, or the long pause can indicate the transition between speakers. On the other hand, a speaker may take a pause in the speech as the speaker gathers further thoughts within the same meaning unit. Therefore, in an embodiment it is desirable to conduct linguistic analysis surrounding, the doublets or triplets of words separated by the pause in the speech. The following is an exemplary acoustic exception as described above which may be used in an embodiment of the method as disclosed herein: -
ratioScore(W k)=P(W k−2 W k−1 </s>)*P(<s>W k)/P(W k−1 W k) - ratioScore(Wk)<MIN_PAUSE_RSCORE if Wk is a silence as returned by the transcription engine or ratioScore(Wk)<MIN_GENERAL_RSCORE otherwise.
- In the above example, ratio score (Wk) is a measure of how related a word (Wk) is to its left context if Wk is a pause then we compare the next term to the right of the kth position Wk+1 with Wk−2 Wk−1 conversely if any of Wk−2 Wk−1 is a pause we look to the next term to the left of Wk. in a merely exemplary embodiment, the following values may be used:
-
MAX_UTT_SIZE=20; MIN_UTT_SIZE=4; MIN_UTT_FOR_SPLIT=15; MIN_MU_SPLIT=4; MIN_PROB=−6; MIN_PAUSE_LEN=60; MIN_PROBABILITY_DIFF=0.15; MIN_BOUNDARY_PROBABILITY=2.05; MIN_PAUSE_RSSCORE=0.0; MIN_GENERAL_RSCORE=0.75 - If the identified word, Wk, is confirmed to be the boundary of the meaning unit then the window is split into two resulting utterances W1. . . Wk and Wk+1 . . . WN. Each of the identified new utterances from 114 is evaluated at 120 to determine if the meaning unit is a longer than a. maximum meaning, unit threshold. If a new utterance is not longer than a maximum meaning unit threshold, then the new utterance is identified at 108 as a meaning unit. If the new utterance is identified as being a longer than a maximum meaning unit threshold at 120, then the steps 110-120 are repeated in order to further identify meaning units within the new utterance. In an alternative embodiment, rather than the comparing the new utterance length to a maximum meaning unit threshold, the new utterance length may be compared to a minimum utterance length for splitting.
- The
method 100 is repeated until all of the transcription has been divided into meaning units. The division of the transcription into meaning units can assist with improving the quality of the analysis achieved by speech analytics. In speech analytics, the already identified meeting units can form the basis for father high-level analysis, such as identifying, themes within the transcription, flow within the transcription, or relations between meeting units in the transcription. - In a non-limiting embodiment, relations within the transcription may be combinations of closely spaced words that convey an idea. As an example, a relation may be [action, object] such as [pay, bill]. With the meaning units already automatedly identified, the speech analytics can search for a relation specifically with in a meaning unit, or specific meeting units, rather than across the entire transcription.
- The functional block diagrams, operational sequences, and flow diagrams provided in the Figures are representative of exemplary architectures, environments, and methodologies for performing novel aspects of the disclosure. While, for purposes of simplicity of explanation, the methodologies included herein may be in the form of a functional diagram, operational sequence, or flow diagram, and may be described as a series of acts, it is to be understood and appreciated that the methodologies are not limited by the order of acts, as some acts may, in accordance therewith, occur in a different order and/or concurrently with other acts from that shown and described herein. For example, those skilled in the art will understand and appreciate that a methodology can alternatively be represented as a series of interrelated states or events, such as in a state diagram. Moreover, not all acts illustrated in a methodology may be required for a novel implementation.
- This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to make and use the invention. The patentable scope of the invention is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements with insubstantial differences from the literal languages of the claims.
Claims (20)
1. A method of zoning a transcription of audio data, the method comprising:
separating the transcription of audio data into a plurality of utterances;
identifying utterances of the plurality of utterances that are shorter than a predetermined minimum threshold as meaning units;
calculating a probability that each word in an utterance of the plurality of utterances which is longer than the predetermined minimum threshold is a meaning unit boundary;
splitting the utterance longer than the predetermined minimum threshold into two new utterances at a word with a maximum calculated probability; and
identifying at least one of the two utterances that is shorter than a maximum utterance threshold as a meaning unit.
2. The method of claim 1 , wherein calculating the probability that each word in the utterance longer than the predetermined minimum threshold is a meaning unit boundary is further based upon at least a linguistic model.
3. The method of claim 2 , wherein the linguistic model comprises statistics, distributions, or frequencies of word pairs or word triplets.
4. The method of claim 2 , wherein the linguistic model comprises probability of words to form the beginning or end of a meaning unit.
5. The method of claim 2 wherein calculating the probability that each word in the utterance longer than the predetermined minimum threshold is a meaning unit boundary is further based upon an acoustic model.
6. The method of claim 2 , further comprising receiving audio data and decoding the audio data to create the transcription of audio data.
7. The method of claim 5 , wherein at least the linguistic model is used when decoding the audio data to create the transcription of audio data.
8. The method of claim 1 , further comprising applying speech analytics to the meaning unit to identify at least one of context or content of the meaning unit.
9. The method of claim 1 , further comprising applying speech analytics to identified meaning units to group the meaning units into call segments.
10. The method of claim 9 , further comprising applying speech analytics to the identified meaning units to identify dialog acts within the identified meaning units.
11. The method of claim 1 , wherein the predetermined minimum threshold is thirty words.
12. The method of claim 1 , further comprising:
selecting utterances of the plurality that are longer than the predetermined minimum threshold for subdivision; and
splitting the selected utterances of the plurality into widows, each window being twice the maximum utterance threshold.
13. The method of claim 12 , wherein calculating the probability that each word in an utterance longer than the predetermined minimum threshold is a meaning unit boundary is calculated for each word in each window.
14. The method of claim 13 , further comprising applying at least one of a linguistic exception and an acoustic exception to the two new utterances.
15. The method of claim 14 , wherein the at least one linguistic exception comprises a minimum meaning unit boundary probability or a minimum meaning unit boundary probability differential.
16. The method of claim 14 , wherein the at least one acoustic exception comprises an identification of a pause between adjacent utterances in the transcription of the audio data.
17. A method of zoning, a transcription of audio data, the method comprising:
separating the transcription of audio data into a plurality of utterances
identifying utterances of the plurality of utterances that are shorter than a predetermined minimum threshold as meaning units;
selecting utterances of the plurality of utterances that are longer than the predetermined minimum threshold for subdivision;
splitting the selected utterances into widows, each window being twice a maximum utterance threshold;
calculating a probability that each word in the plurality of windows is a meaning unit boundary based upon at least a linguistic model applied to each of the plurality of windows;
splitting the selected utterances which are longer than the predetermined minimum threshold into two new utterances at a word with a maximum calculated probability; and
identifying at least one of the two new utterances that is shorter than a maximum utterance threshold as a meaning unit.
18. The method of claim 17 , wherein the linguistic model comprises probability of words to form the beginning or end of a meaning unit.
19. The method of claim 18 , further comprising receiving audio data and decoding the audio data with at least the linguistic model to create the transcription of audio data.
20. The method of claim 19 , further comprising applying at least one of a linguistic exception and an acoustic exception to the two new utterances.
Priority Applications (4)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US14/467,783 US20150066506A1 (en) | 2013-08-30 | 2014-08-25 | System and Method of Text Zoning |
EP14182714.7A EP2849177B1 (en) | 2013-08-30 | 2014-08-28 | System and method of text zoning |
US16/553,451 US11217252B2 (en) | 2013-08-30 | 2019-08-28 | System and method of text zoning |
US17/567,491 US11900943B2 (en) | 2013-08-30 | 2022-01-03 | System and method of text zoning |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US201361872224P | 2013-08-30 | 2013-08-30 | |
US14/467,783 US20150066506A1 (en) | 2013-08-30 | 2014-08-25 | System and Method of Text Zoning |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US16/553,451 Continuation US11217252B2 (en) | 2013-08-30 | 2019-08-28 | System and method of text zoning |
Publications (1)
Publication Number | Publication Date |
---|---|
US20150066506A1 true US20150066506A1 (en) | 2015-03-05 |
Family
ID=51518528
Family Applications (3)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/467,783 Abandoned US20150066506A1 (en) | 2013-08-30 | 2014-08-25 | System and Method of Text Zoning |
US16/553,451 Active 2034-12-23 US11217252B2 (en) | 2013-08-30 | 2019-08-28 | System and method of text zoning |
US17/567,491 Active US11900943B2 (en) | 2013-08-30 | 2022-01-03 | System and method of text zoning |
Family Applications After (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US16/553,451 Active 2034-12-23 US11217252B2 (en) | 2013-08-30 | 2019-08-28 | System and method of text zoning |
US17/567,491 Active US11900943B2 (en) | 2013-08-30 | 2022-01-03 | System and method of text zoning |
Country Status (2)
Country | Link |
---|---|
US (3) | US20150066506A1 (en) |
EP (1) | EP2849177B1 (en) |
Cited By (148)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20150348551A1 (en) * | 2014-05-30 | 2015-12-03 | Apple Inc. | Multi-command single utterance input method |
US20170053643A1 (en) * | 2015-08-19 | 2017-02-23 | International Business Machines Corporation | Adaptation of speech recognition |
US9668024B2 (en) | 2014-06-30 | 2017-05-30 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US9865248B2 (en) | 2008-04-05 | 2018-01-09 | Apple Inc. | Intelligent text-to-speech conversion |
US9934775B2 (en) | 2016-05-26 | 2018-04-03 | Apple Inc. | Unit-selection text-to-speech synthesis based on predicted concatenation parameters |
US9966060B2 (en) | 2013-06-07 | 2018-05-08 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
US9972304B2 (en) | 2016-06-03 | 2018-05-15 | Apple Inc. | Privacy preserving distributed evaluation framework for embedded personalized systems |
US9971774B2 (en) | 2012-09-19 | 2018-05-15 | Apple Inc. | Voice-based media searching |
US9986419B2 (en) | 2014-09-30 | 2018-05-29 | Apple Inc. | Social reminders |
US10043516B2 (en) | 2016-09-23 | 2018-08-07 | Apple Inc. | Intelligent automated assistant |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
US10049675B2 (en) | 2010-02-25 | 2018-08-14 | Apple Inc. | User profiling for voice input processing |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
US10079014B2 (en) | 2012-06-08 | 2018-09-18 | Apple Inc. | Name recognition system |
US10083690B2 (en) | 2014-05-30 | 2018-09-25 | Apple Inc. | Better resolution when referencing to concepts |
US10089072B2 (en) | 2016-06-11 | 2018-10-02 | Apple Inc. | Intelligent device arbitration and control |
US10108612B2 (en) | 2008-07-31 | 2018-10-23 | Apple Inc. | Mobile device having human language translation capability with positional feedback |
US10169329B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Exemplar-based natural language processing |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10249300B2 (en) | 2016-06-06 | 2019-04-02 | Apple Inc. | Intelligent list reading |
US10255346B2 (en) | 2014-01-31 | 2019-04-09 | Verint Systems Ltd. | Tagging relations with N-best |
US10269345B2 (en) | 2016-06-11 | 2019-04-23 | Apple Inc. | Intelligent task discovery |
US10283110B2 (en) | 2009-07-02 | 2019-05-07 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
US10297253B2 (en) | 2016-06-11 | 2019-05-21 | Apple Inc. | Application integration with a digital assistant |
US10303715B2 (en) | 2017-05-16 | 2019-05-28 | Apple Inc. | Intelligent automated assistant for media exploration |
US10311144B2 (en) | 2017-05-16 | 2019-06-04 | Apple Inc. | Emoji word sense disambiguation |
US10311871B2 (en) | 2015-03-08 | 2019-06-04 | Apple Inc. | Competing devices responding to voice triggers |
US10318871B2 (en) | 2005-09-08 | 2019-06-11 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US10332518B2 (en) | 2017-05-09 | 2019-06-25 | Apple Inc. | User interface for correcting recognition errors |
US10339452B2 (en) | 2013-02-06 | 2019-07-02 | Verint Systems Ltd. | Automated ontology development |
US10356243B2 (en) | 2015-06-05 | 2019-07-16 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US10354011B2 (en) | 2016-06-09 | 2019-07-16 | Apple Inc. | Intelligent automated assistant in a home environment |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US10381016B2 (en) | 2008-01-03 | 2019-08-13 | Apple Inc. | Methods and apparatus for altering audio output signals |
US10395654B2 (en) | 2017-05-11 | 2019-08-27 | Apple Inc. | Text normalization based on a data-driven learning network |
US10403283B1 (en) | 2018-06-01 | 2019-09-03 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US10403278B2 (en) | 2017-05-16 | 2019-09-03 | Apple Inc. | Methods and systems for phonetic matching in digital assistant services |
US10410637B2 (en) | 2017-05-12 | 2019-09-10 | Apple Inc. | User-specific acoustic models |
US10417405B2 (en) | 2011-03-21 | 2019-09-17 | Apple Inc. | Device access using voice authentication |
US10417266B2 (en) | 2017-05-09 | 2019-09-17 | Apple Inc. | Context-aware ranking of intelligent response suggestions |
US10431204B2 (en) | 2014-09-11 | 2019-10-01 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10438595B2 (en) | 2014-09-30 | 2019-10-08 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US10445429B2 (en) | 2017-09-21 | 2019-10-15 | Apple Inc. | Natural language understanding using vocabularies with compressed serialized tries |
US10446143B2 (en) | 2016-03-14 | 2019-10-15 | Apple Inc. | Identification of voice inputs providing credentials |
US10453443B2 (en) | 2014-09-30 | 2019-10-22 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US10474753B2 (en) | 2016-09-07 | 2019-11-12 | Apple Inc. | Language identification using recurrent neural networks |
US10482874B2 (en) | 2017-05-15 | 2019-11-19 | Apple Inc. | Hierarchical belief states for digital assistants |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10496705B1 (en) | 2018-06-03 | 2019-12-03 | Apple Inc. | Accelerated task performance |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
US10521466B2 (en) | 2016-06-11 | 2019-12-31 | Apple Inc. | Data driven natural language event detection and classification |
US10529332B2 (en) | 2015-03-08 | 2020-01-07 | Apple Inc. | Virtual assistant activation |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US10592604B2 (en) | 2018-03-12 | 2020-03-17 | Apple Inc. | Inverse text normalization for automatic speech recognition |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
US10636424B2 (en) | 2017-11-30 | 2020-04-28 | Apple Inc. | Multi-turn canned dialog |
US10643611B2 (en) | 2008-10-02 | 2020-05-05 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US10650803B2 (en) | 2017-10-10 | 2020-05-12 | International Business Machines Corporation | Mapping between speech signal and transcript |
US10657961B2 (en) | 2013-06-08 | 2020-05-19 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US10657328B2 (en) | 2017-06-02 | 2020-05-19 | Apple Inc. | Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US10684703B2 (en) | 2018-06-01 | 2020-06-16 | Apple Inc. | Attention aware virtual assistant dismissal |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US10699717B2 (en) | 2014-05-30 | 2020-06-30 | Apple Inc. | Intelligent assistant for home automation |
US10706841B2 (en) | 2010-01-18 | 2020-07-07 | Apple Inc. | Task flow identification based on user intent |
US10714117B2 (en) | 2013-02-07 | 2020-07-14 | Apple Inc. | Voice trigger for a digital assistant |
US10726832B2 (en) | 2017-05-11 | 2020-07-28 | Apple Inc. | Maintaining privacy of personal information |
US10733375B2 (en) | 2018-01-31 | 2020-08-04 | Apple Inc. | Knowledge-based framework for improving natural language understanding |
US10733982B2 (en) | 2018-01-08 | 2020-08-04 | Apple Inc. | Multi-directional dialog |
US10733993B2 (en) | 2016-06-10 | 2020-08-04 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10741185B2 (en) | 2010-01-18 | 2020-08-11 | Apple Inc. | Intelligent automated assistant |
US10748546B2 (en) | 2017-05-16 | 2020-08-18 | Apple Inc. | Digital assistant services based on device capabilities |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US10755703B2 (en) | 2017-05-11 | 2020-08-25 | Apple Inc. | Offline personal assistant |
US10755051B2 (en) | 2017-09-29 | 2020-08-25 | Apple Inc. | Rule-based natural language processing |
US10769385B2 (en) | 2013-06-09 | 2020-09-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
US10791176B2 (en) | 2017-05-12 | 2020-09-29 | Apple Inc. | Synchronization and task delegation of a digital assistant |
US10789959B2 (en) | 2018-03-02 | 2020-09-29 | Apple Inc. | Training speaker recognition models for digital assistants |
US10789945B2 (en) | 2017-05-12 | 2020-09-29 | Apple Inc. | Low-latency intelligent automated assistant |
US10795541B2 (en) | 2009-06-05 | 2020-10-06 | Apple Inc. | Intelligent organization of tasks items |
US10810274B2 (en) | 2017-05-15 | 2020-10-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
US10818288B2 (en) | 2018-03-26 | 2020-10-27 | Apple Inc. | Natural assistant interaction |
US10839159B2 (en) | 2018-09-28 | 2020-11-17 | Apple Inc. | Named entity normalization in a spoken dialog system |
US10892996B2 (en) | 2018-06-01 | 2021-01-12 | Apple Inc. | Variable latency device coordination |
US10909331B2 (en) | 2018-03-30 | 2021-02-02 | Apple Inc. | Implicit identification of translation payload with neural machine translation |
US10928918B2 (en) | 2018-05-07 | 2021-02-23 | Apple Inc. | Raise to speak |
US20210097998A1 (en) * | 2017-05-16 | 2021-04-01 | Apple Inc. | Detecting a trigger of a digital assistant |
US10984780B2 (en) | 2018-05-21 | 2021-04-20 | Apple Inc. | Global semantic word embeddings using bi-directional recurrent neural networks |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US11010127B2 (en) | 2015-06-29 | 2021-05-18 | Apple Inc. | Virtual assistant for media playback |
US11010561B2 (en) | 2018-09-27 | 2021-05-18 | Apple Inc. | Sentiment prediction from textual data |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US11023513B2 (en) | 2007-12-20 | 2021-06-01 | Apple Inc. | Method and apparatus for searching using an active ontology |
US11030406B2 (en) | 2015-01-27 | 2021-06-08 | Verint Systems Ltd. | Ontology expansion using entity-association rules and abstract relations |
US11048473B2 (en) | 2013-06-09 | 2021-06-29 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US11070949B2 (en) | 2015-05-27 | 2021-07-20 | Apple Inc. | Systems and methods for proactively identifying and surfacing relevant content on an electronic device with a touch-sensitive display |
US11069336B2 (en) | 2012-03-02 | 2021-07-20 | Apple Inc. | Systems and methods for name pronunciation |
US11080012B2 (en) | 2009-06-05 | 2021-08-03 | Apple Inc. | Interface for a virtual digital assistant |
US11120372B2 (en) | 2011-06-03 | 2021-09-14 | Apple Inc. | Performing actions associated with task items that represent tasks to perform |
US11127397B2 (en) | 2015-05-27 | 2021-09-21 | Apple Inc. | Device voice control |
US11133008B2 (en) | 2014-05-30 | 2021-09-28 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US11140099B2 (en) | 2019-05-21 | 2021-10-05 | Apple Inc. | Providing message response suggestions |
US11145294B2 (en) | 2018-05-07 | 2021-10-12 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US11170166B2 (en) | 2018-09-28 | 2021-11-09 | Apple Inc. | Neural typographical error modeling via generative adversarial networks |
US11204787B2 (en) | 2017-01-09 | 2021-12-21 | Apple Inc. | Application integration with a digital assistant |
US11217252B2 (en) | 2013-08-30 | 2022-01-04 | Verint Systems Inc. | System and method of text zoning |
US11217251B2 (en) | 2019-05-06 | 2022-01-04 | Apple Inc. | Spoken notifications |
US11227589B2 (en) | 2016-06-06 | 2022-01-18 | Apple Inc. | Intelligent list reading |
US11231904B2 (en) | 2015-03-06 | 2022-01-25 | Apple Inc. | Reducing response latency of intelligent automated assistants |
US11237797B2 (en) | 2019-05-31 | 2022-02-01 | Apple Inc. | User activity shortcut suggestions |
US11269678B2 (en) | 2012-05-15 | 2022-03-08 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US11281993B2 (en) | 2016-12-05 | 2022-03-22 | Apple Inc. | Model and ensemble compression for metric learning |
US11289073B2 (en) | 2019-05-31 | 2022-03-29 | Apple Inc. | Device text to speech |
US11301477B2 (en) | 2017-05-12 | 2022-04-12 | Apple Inc. | Feedback analysis of a digital assistant |
US11307752B2 (en) | 2019-05-06 | 2022-04-19 | Apple Inc. | User configurable task triggers |
US11314370B2 (en) | 2013-12-06 | 2022-04-26 | Apple Inc. | Method for extracting salient dialog usage from live data |
US11348573B2 (en) | 2019-03-18 | 2022-05-31 | Apple Inc. | Multimodality in digital assistant systems |
US11350253B2 (en) | 2011-06-03 | 2022-05-31 | Apple Inc. | Active transport based notifications |
US11360641B2 (en) | 2019-06-01 | 2022-06-14 | Apple Inc. | Increasing the relevance of new available information |
US11361161B2 (en) | 2018-10-22 | 2022-06-14 | Verint Americas Inc. | Automated system and method to prioritize language model and ontology expansion and pruning |
US11388291B2 (en) | 2013-03-14 | 2022-07-12 | Apple Inc. | System and method for processing voicemail |
US11386266B2 (en) | 2018-06-01 | 2022-07-12 | Apple Inc. | Text correction |
US11423908B2 (en) | 2019-05-06 | 2022-08-23 | Apple Inc. | Interpreting spoken requests |
US20220277733A1 (en) * | 2019-08-14 | 2022-09-01 | Unify Patente Gmbh & Co. Kg | Real-time communication and collaboration system and method of monitoring objectives to be achieved by a plurality of users collaborating on a real-time communication and collaboration platform |
US11462215B2 (en) | 2018-09-28 | 2022-10-04 | Apple Inc. | Multi-modal inputs for voice commands |
US11468282B2 (en) | 2015-05-15 | 2022-10-11 | Apple Inc. | Virtual assistant in a communication session |
US11467802B2 (en) | 2017-05-11 | 2022-10-11 | Apple Inc. | Maintaining privacy of personal information |
US11475898B2 (en) | 2018-10-26 | 2022-10-18 | Apple Inc. | Low-latency multi-speaker speech recognition |
US11475884B2 (en) | 2019-05-06 | 2022-10-18 | Apple Inc. | Reducing digital assistant latency when a language is incorrectly determined |
US11488406B2 (en) | 2019-09-25 | 2022-11-01 | Apple Inc. | Text detection using global geometry estimators |
US11496600B2 (en) | 2019-05-31 | 2022-11-08 | Apple Inc. | Remote execution of machine-learned models |
US11495218B2 (en) | 2018-06-01 | 2022-11-08 | Apple Inc. | Virtual assistant operation in multi-device environments |
US11638059B2 (en) | 2019-01-04 | 2023-04-25 | Apple Inc. | Content playback on multiple devices |
US11657813B2 (en) | 2019-05-31 | 2023-05-23 | Apple Inc. | Voice identification in digital assistant systems |
US11671920B2 (en) | 2007-04-03 | 2023-06-06 | Apple Inc. | Method and system for operating a multifunction portable electronic device using voice-activation |
US11696060B2 (en) | 2020-07-21 | 2023-07-04 | Apple Inc. | User identification using headphones |
US11765209B2 (en) | 2020-05-11 | 2023-09-19 | Apple Inc. | Digital assistant hardware abstraction |
US11769012B2 (en) | 2019-03-27 | 2023-09-26 | Verint Americas Inc. | Automated system and method to prioritize language model and ontology expansion and pruning |
US11790914B2 (en) | 2019-06-01 | 2023-10-17 | Apple Inc. | Methods and user interfaces for voice-based control of electronic devices |
US11798547B2 (en) | 2013-03-15 | 2023-10-24 | Apple Inc. | Voice activated device for use with a voice-based digital assistant |
US11809483B2 (en) | 2015-09-08 | 2023-11-07 | Apple Inc. | Intelligent automated assistant for media search and playback |
US11838734B2 (en) | 2020-07-20 | 2023-12-05 | Apple Inc. | Multi-device audio adjustment coordination |
US11841890B2 (en) | 2014-01-31 | 2023-12-12 | Verint Systems Inc. | Call summary |
US11853536B2 (en) | 2015-09-08 | 2023-12-26 | Apple Inc. | Intelligent automated assistant in a media environment |
US11886805B2 (en) | 2015-11-09 | 2024-01-30 | Apple Inc. | Unconventional virtual assistant interactions |
US11914848B2 (en) | 2020-05-11 | 2024-02-27 | Apple Inc. | Providing relevant data items based on context |
Families Citing this family (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US11755276B2 (en) | 2020-05-12 | 2023-09-12 | Apple Inc. | Reducing description length based on confidence |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5737617A (en) * | 1995-06-06 | 1998-04-07 | International Business Machines Corporation | Method and system for English text analysis |
US6718296B1 (en) * | 1998-10-08 | 2004-04-06 | British Telecommunications Public Limited Company | Measurement of signal quality |
US20090150139A1 (en) * | 2007-12-10 | 2009-06-11 | Kabushiki Kaisha Toshiba | Method and apparatus for translating a speech |
Family Cites Families (92)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5317673A (en) | 1992-06-22 | 1994-05-31 | Sri International | Method and apparatus for context-dependent estimation of multiple probability distributions of phonetic classes with multilayer perceptrons in a speech recognition system |
US6076088A (en) | 1996-02-09 | 2000-06-13 | Paik; Woojin | Information extraction system and method using concept relation concept (CRC) triples |
US7113958B1 (en) | 1996-08-12 | 2006-09-26 | Battelle Memorial Institute | Three-dimensional display of document set |
US6385579B1 (en) | 1999-04-29 | 2002-05-07 | International Business Machines Corporation | Methods and apparatus for forming compound words for use in a continuous speech recognition system |
AU5451800A (en) | 1999-05-28 | 2000-12-18 | Sehda, Inc. | Phrase-based dialogue modeling with particular application to creating recognition grammars for voice-controlled user interfaces |
US20020032564A1 (en) | 2000-04-19 | 2002-03-14 | Farzad Ehsani | Phrase-based dialogue modeling with particular application to creating a recognition grammar for a voice-controlled user interface |
US8051104B2 (en) | 1999-09-22 | 2011-11-01 | Google Inc. | Editing a network of interconnected concepts |
US6542866B1 (en) * | 1999-09-22 | 2003-04-01 | Microsoft Corporation | Speech recognition method and apparatus utilizing multiple feature streams |
US6600821B1 (en) | 1999-10-26 | 2003-07-29 | Rockwell Electronic Commerce Corp. | System and method for automatically detecting problematic calls |
US6434557B1 (en) | 1999-12-30 | 2002-08-13 | Decode Genetics Ehf. | Online syntheses programming technique |
US6560590B1 (en) | 2000-02-14 | 2003-05-06 | Kana Software, Inc. | Method and apparatus for multiple tiered matching of natural language queries to positions in a text corpus |
DE10022586A1 (en) | 2000-05-09 | 2001-11-22 | Siemens Ag | Generating speech database for target vocabulary involves concatenating training text segments with target vocabulary words according to phonetic description |
AU2001293596A1 (en) | 2000-09-29 | 2002-04-08 | Gavagai Technology Incorporated | A method and system for adapting synonym resources to specific domains |
US6721728B2 (en) | 2001-03-02 | 2004-04-13 | The United States Of America As Represented By The Administrator Of The National Aeronautics And Space Administration | System, method and apparatus for discovering phrases in a database |
US7269546B2 (en) | 2001-05-09 | 2007-09-11 | International Business Machines Corporation | System and method of finding documents related to other documents and of finding related words in response to a query to refine a search |
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 |
US7243092B2 (en) | 2001-12-28 | 2007-07-10 | Sap Ag | Taxonomy generation for electronic documents |
US7853544B2 (en) | 2004-11-24 | 2010-12-14 | Overtone, Inc. | Systems and methods for automatically categorizing unstructured text |
US7877383B2 (en) | 2005-04-27 | 2011-01-25 | Microsoft Corporation | Ranking and accessing definitions of terms |
US7912701B1 (en) | 2005-05-04 | 2011-03-22 | IgniteIP Capital IA Special Management LLC | Method and apparatus for semiotic correlation |
US20070016863A1 (en) | 2005-07-08 | 2007-01-18 | Yan Qu | Method and apparatus for extracting and structuring domain terms |
US7552053B2 (en) | 2005-08-22 | 2009-06-23 | International Business Machines Corporation | Techniques for aiding speech-to-speech translation |
US8036876B2 (en) | 2005-11-04 | 2011-10-11 | Battelle Memorial Institute | Methods of defining ontologies, word disambiguation methods, computer systems, and articles of manufacture |
US7587308B2 (en) | 2005-11-21 | 2009-09-08 | Hewlett-Packard Development Company, L.P. | Word recognition using ontologies |
US8725518B2 (en) * | 2006-04-25 | 2014-05-13 | Nice Systems Ltd. | Automatic speech analysis |
US7987088B2 (en) | 2006-07-24 | 2011-07-26 | Lockheed Martin Corporation | System and method for automating the generation of an ontology from unstructured documents |
US8160232B2 (en) | 2006-08-31 | 2012-04-17 | Kana Software, Inc. | Dynamic message context driven application assembly for customer service agent productivity applications |
US8396878B2 (en) | 2006-09-22 | 2013-03-12 | Limelight Networks, Inc. | Methods and systems for generating automated tags for video files |
US7630981B2 (en) | 2006-12-26 | 2009-12-08 | Robert Bosch Gmbh | Method and system for learning ontological relations from documents |
US20080221882A1 (en) * | 2007-03-06 | 2008-09-11 | Bundock Donald S | System for excluding unwanted data from a voice recording |
US7904414B2 (en) | 2007-04-02 | 2011-03-08 | Kana Software, Inc. | Adaptive multi-channel answering service for knowledge management systems |
WO2008134588A1 (en) | 2007-04-25 | 2008-11-06 | Counsyl, Inc. | Methods and systems of automatic ontology population |
US8078565B2 (en) | 2007-06-12 | 2011-12-13 | Kana Software, Inc. | Organically ranked knowledge categorization in a knowledge management system |
US8260809B2 (en) | 2007-06-28 | 2012-09-04 | Microsoft Corporation | Voice-based search processing |
US8452725B2 (en) | 2008-09-03 | 2013-05-28 | Hamid Hatami-Hanza | System and method of ontological subject mapping for knowledge processing applications |
US8209171B2 (en) | 2007-08-07 | 2012-06-26 | Aurix Limited | Methods and apparatus relating to searching of spoken audio data |
US8165985B2 (en) | 2007-10-12 | 2012-04-24 | Palo Alto Research Center Incorporated | System and method for performing discovery of digital information in a subject area |
US8190628B1 (en) | 2007-11-30 | 2012-05-29 | Google Inc. | Phrase generation |
US8280886B2 (en) | 2008-02-13 | 2012-10-02 | Fujitsu Limited | Determining candidate terms related to terms of a query |
US9355354B2 (en) | 2008-02-15 | 2016-05-31 | Verint Americas Inc. | Embedded multi-channel knowledgebase |
US8752005B2 (en) | 2008-04-04 | 2014-06-10 | Infosys Limited | Concept-oriented software engineering system and method for identifying, extracting, organizing, inferring and querying software system facts |
US8417513B2 (en) | 2008-06-06 | 2013-04-09 | Radiant Logic Inc. | Representation of objects and relationships in databases, directories, web services, and applications as sentences as a method to represent context in structured data |
WO2009158581A2 (en) | 2008-06-27 | 2009-12-30 | Adpassage, Inc. | System and method for spoken topic or criterion recognition in digital media and contextual advertising |
US8359191B2 (en) | 2008-08-01 | 2013-01-22 | International Business Machines Corporation | Deriving ontology based on linguistics and community tag clouds |
US8751531B2 (en) | 2008-08-29 | 2014-06-10 | Nec Corporation | Text mining apparatus, text mining method, and computer-readable recording medium |
US20100057688A1 (en) | 2008-09-04 | 2010-03-04 | Kana Software, Inc. | Adaptive multi-channel answering service for knowledge management systems |
US8374881B2 (en) * | 2008-11-26 | 2013-02-12 | At&T Intellectual Property I, L.P. | System and method for enriching spoken language translation with dialog acts |
US20100161604A1 (en) | 2008-12-23 | 2010-06-24 | Nice Systems Ltd | Apparatus and method for multimedia content based manipulation |
JP5536518B2 (en) | 2009-04-23 | 2014-07-02 | 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 | Method, apparatus and computer for automatically extracting a system modeling metamodel language model for the system from the natural language specification of the system |
TWI396184B (en) * | 2009-09-17 | 2013-05-11 | Tze Fen Li | A method for speech recognition on all languages and for inputing words using speech recognition |
TWI430189B (en) | 2009-11-10 | 2014-03-11 | Inst Information Industry | System, apparatus and method for message simulation |
US20130166303A1 (en) | 2009-11-13 | 2013-06-27 | Adobe Systems Incorporated | Accessing media data using metadata repository |
US8751218B2 (en) | 2010-02-09 | 2014-06-10 | Siemens Aktiengesellschaft | Indexing content at semantic level |
US9684683B2 (en) | 2010-02-09 | 2017-06-20 | Siemens Aktiengesellschaft | Semantic search tool for document tagging, indexing and search |
US9066049B2 (en) | 2010-04-12 | 2015-06-23 | Adobe Systems Incorporated | Method and apparatus for processing scripts |
US8874432B2 (en) | 2010-04-28 | 2014-10-28 | Nec Laboratories America, Inc. | Systems and methods for semi-supervised relationship extraction |
US20120016671A1 (en) * | 2010-07-15 | 2012-01-19 | Pawan Jaggi | Tool and method for enhanced human machine collaboration for rapid and accurate transcriptions |
US8521672B2 (en) | 2010-11-22 | 2013-08-27 | Microsoft Corporation | Dependency-based query expansion alteration candidate scoring |
US9135241B2 (en) | 2010-12-08 | 2015-09-15 | At&T Intellectual Property I, L.P. | System and method for learning latent representations for natural language tasks |
CA2741212C (en) | 2011-05-27 | 2020-12-08 | Ibm Canada Limited - Ibm Canada Limitee | Automated self-service user support based on ontology analysis |
US20130018650A1 (en) | 2011-07-11 | 2013-01-17 | Microsoft Corporation | Selection of Language Model Training Data |
US8918431B2 (en) | 2011-09-09 | 2014-12-23 | Sri International | Adaptive ontology |
US8620964B2 (en) | 2011-11-21 | 2013-12-31 | Motorola Mobility Llc | Ontology construction |
US8747115B2 (en) | 2012-03-28 | 2014-06-10 | International Business Machines Corporation | Building an ontology by transforming complex triples |
US10109278B2 (en) * | 2012-08-02 | 2018-10-23 | Audible, Inc. | Aligning body matter across content formats |
US9461876B2 (en) | 2012-08-29 | 2016-10-04 | Loci | System and method for fuzzy concept mapping, voting ontology crowd sourcing, and technology prediction |
US11568420B2 (en) | 2012-11-21 | 2023-01-31 | Verint Americas Inc. | Analysis of customer feedback surveys |
US9646605B2 (en) | 2013-01-22 | 2017-05-09 | Interactive Intelligence Group, Inc. | False alarm reduction in speech recognition systems using contextual information |
IL224482B (en) | 2013-01-29 | 2018-08-30 | Verint Systems Ltd | System and method for keyword spotting using representative dictionary |
US10339452B2 (en) | 2013-02-06 | 2019-07-02 | Verint Systems Ltd. | Automated ontology development |
US9262935B2 (en) | 2013-02-15 | 2016-02-16 | Voxy, Inc. | Systems and methods for extracting keywords in language learning |
US9760546B2 (en) | 2013-05-24 | 2017-09-12 | Xerox Corporation | Identifying repeat subsequences by left and right contexts |
US10061822B2 (en) | 2013-07-26 | 2018-08-28 | Genesys Telecommunications Laboratories, Inc. | System and method for discovering and exploring concepts and root causes of events |
US20150066506A1 (en) | 2013-08-30 | 2015-03-05 | Verint Systems Ltd. | System and Method of Text Zoning |
WO2015035401A1 (en) | 2013-09-09 | 2015-03-12 | Ayasdi, Inc. | Automated discovery using textual analysis |
US9477752B1 (en) | 2013-09-30 | 2016-10-25 | Verint Systems Inc. | Ontology administration and application to enhance communication data analytics |
US9697246B1 (en) | 2013-09-30 | 2017-07-04 | Verint Systems Ltd. | Themes surfacing for communication data analysis |
US9232063B2 (en) | 2013-10-31 | 2016-01-05 | Verint Systems Inc. | Call flow and discourse analysis |
US10078689B2 (en) | 2013-10-31 | 2018-09-18 | Verint Systems Ltd. | Labeling/naming of themes |
FR3015073A1 (en) | 2013-12-18 | 2015-06-19 | Wepingo | METHOD AND DEVICE FOR AUTOMATICALLY RECOMMENDING COMPLEX OBJECTS |
US10191978B2 (en) | 2014-01-03 | 2019-01-29 | Verint Systems Ltd. | Labeling/naming of themes |
US20150220946A1 (en) | 2014-01-31 | 2015-08-06 | Verint Systems Ltd. | System and Method of Trend Identification |
US9817892B2 (en) | 2014-01-31 | 2017-11-14 | Verint Systems Ltd. | Automated removal of private information |
US10255346B2 (en) | 2014-01-31 | 2019-04-09 | Verint Systems Ltd. | Tagging relations with N-best |
US9569743B2 (en) | 2014-01-31 | 2017-02-14 | Verint Systems Ltd. | Funnel analysis |
US9977830B2 (en) | 2014-01-31 | 2018-05-22 | Verint Systems Ltd. | Call summary |
US9575936B2 (en) | 2014-07-17 | 2017-02-21 | Verint Systems Ltd. | Word cloud display |
US9786276B2 (en) | 2014-08-25 | 2017-10-10 | Honeywell International Inc. | Speech enabled management system |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US20160078016A1 (en) | 2014-09-12 | 2016-03-17 | General Electric Company | Intelligent ontology update tool |
US9846901B2 (en) | 2014-12-18 | 2017-12-19 | Nuance Communications, Inc. | Product recommendation with ontology-linked product review |
US20160217127A1 (en) | 2015-01-27 | 2016-07-28 | Verint Systems Ltd. | Identification of significant phrases using multiple language models |
-
2014
- 2014-08-25 US US14/467,783 patent/US20150066506A1/en not_active Abandoned
- 2014-08-28 EP EP14182714.7A patent/EP2849177B1/en active Active
-
2019
- 2019-08-28 US US16/553,451 patent/US11217252B2/en active Active
-
2022
- 2022-01-03 US US17/567,491 patent/US11900943B2/en active Active
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US5737617A (en) * | 1995-06-06 | 1998-04-07 | International Business Machines Corporation | Method and system for English text analysis |
US6718296B1 (en) * | 1998-10-08 | 2004-04-06 | British Telecommunications Public Limited Company | Measurement of signal quality |
US20090150139A1 (en) * | 2007-12-10 | 2009-06-11 | Kabushiki Kaisha Toshiba | Method and apparatus for translating a speech |
Non-Patent Citations (1)
Title |
---|
Stolcke, Andreas, et al., "Automatic Linguistic Segmentation of Conversational Speech," IEEE, Vol. 2, 1996, pp. 1005-1008. * |
Cited By (236)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US10318871B2 (en) | 2005-09-08 | 2019-06-11 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US11928604B2 (en) | 2005-09-08 | 2024-03-12 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US11671920B2 (en) | 2007-04-03 | 2023-06-06 | Apple Inc. | Method and system for operating a multifunction portable electronic device using voice-activation |
US11023513B2 (en) | 2007-12-20 | 2021-06-01 | Apple Inc. | Method and apparatus for searching using an active ontology |
US10381016B2 (en) | 2008-01-03 | 2019-08-13 | Apple Inc. | Methods and apparatus for altering audio output signals |
US9865248B2 (en) | 2008-04-05 | 2018-01-09 | Apple Inc. | Intelligent text-to-speech conversion |
US10108612B2 (en) | 2008-07-31 | 2018-10-23 | Apple Inc. | Mobile device having human language translation capability with positional feedback |
US10643611B2 (en) | 2008-10-02 | 2020-05-05 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US11348582B2 (en) | 2008-10-02 | 2022-05-31 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US11900936B2 (en) | 2008-10-02 | 2024-02-13 | Apple Inc. | Electronic devices with voice command and contextual data processing capabilities |
US10795541B2 (en) | 2009-06-05 | 2020-10-06 | Apple Inc. | Intelligent organization of tasks items |
US11080012B2 (en) | 2009-06-05 | 2021-08-03 | Apple Inc. | Interface for a virtual digital assistant |
US10283110B2 (en) | 2009-07-02 | 2019-05-07 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
US11423886B2 (en) | 2010-01-18 | 2022-08-23 | Apple Inc. | Task flow identification based on user intent |
US10741185B2 (en) | 2010-01-18 | 2020-08-11 | Apple Inc. | Intelligent automated assistant |
US10706841B2 (en) | 2010-01-18 | 2020-07-07 | Apple Inc. | Task flow identification based on user intent |
US10692504B2 (en) | 2010-02-25 | 2020-06-23 | Apple Inc. | User profiling for voice input processing |
US10049675B2 (en) | 2010-02-25 | 2018-08-14 | Apple Inc. | User profiling for voice input processing |
US10417405B2 (en) | 2011-03-21 | 2019-09-17 | Apple Inc. | Device access using voice authentication |
US11350253B2 (en) | 2011-06-03 | 2022-05-31 | Apple Inc. | Active transport based notifications |
US11120372B2 (en) | 2011-06-03 | 2021-09-14 | Apple Inc. | Performing actions associated with task items that represent tasks to perform |
US11069336B2 (en) | 2012-03-02 | 2021-07-20 | Apple Inc. | Systems and methods for name pronunciation |
US11321116B2 (en) | 2012-05-15 | 2022-05-03 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US11269678B2 (en) | 2012-05-15 | 2022-03-08 | Apple Inc. | Systems and methods for integrating third party services with a digital assistant |
US10079014B2 (en) | 2012-06-08 | 2018-09-18 | Apple Inc. | Name recognition system |
US9971774B2 (en) | 2012-09-19 | 2018-05-15 | Apple Inc. | Voice-based media searching |
US10339452B2 (en) | 2013-02-06 | 2019-07-02 | Verint Systems Ltd. | Automated ontology development |
US10679134B2 (en) | 2013-02-06 | 2020-06-09 | Verint Systems Ltd. | Automated ontology development |
US10714117B2 (en) | 2013-02-07 | 2020-07-14 | Apple Inc. | Voice trigger for a digital assistant |
US10978090B2 (en) | 2013-02-07 | 2021-04-13 | Apple Inc. | Voice trigger for a digital assistant |
US11636869B2 (en) | 2013-02-07 | 2023-04-25 | Apple Inc. | Voice trigger for a digital assistant |
US11557310B2 (en) | 2013-02-07 | 2023-01-17 | Apple Inc. | Voice trigger for a digital assistant |
US11862186B2 (en) | 2013-02-07 | 2024-01-02 | Apple Inc. | Voice trigger for a digital assistant |
US11388291B2 (en) | 2013-03-14 | 2022-07-12 | Apple Inc. | System and method for processing voicemail |
US11798547B2 (en) | 2013-03-15 | 2023-10-24 | Apple Inc. | Voice activated device for use with a voice-based digital assistant |
US9966060B2 (en) | 2013-06-07 | 2018-05-08 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
US10657961B2 (en) | 2013-06-08 | 2020-05-19 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US10769385B2 (en) | 2013-06-09 | 2020-09-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
US11048473B2 (en) | 2013-06-09 | 2021-06-29 | Apple Inc. | Device, method, and graphical user interface for enabling conversation persistence across two or more instances of a digital assistant |
US11727219B2 (en) | 2013-06-09 | 2023-08-15 | Apple Inc. | System and method for inferring user intent from speech inputs |
US11217252B2 (en) | 2013-08-30 | 2022-01-04 | Verint Systems Inc. | System and method of text zoning |
US11314370B2 (en) | 2013-12-06 | 2022-04-26 | Apple Inc. | Method for extracting salient dialog usage from live data |
US11841890B2 (en) | 2014-01-31 | 2023-12-12 | Verint Systems Inc. | Call summary |
US10255346B2 (en) | 2014-01-31 | 2019-04-09 | Verint Systems Ltd. | Tagging relations with N-best |
US10497365B2 (en) | 2014-05-30 | 2019-12-03 | Apple Inc. | Multi-command single utterance input method |
US11257504B2 (en) | 2014-05-30 | 2022-02-22 | Apple Inc. | Intelligent assistant for home automation |
US10417344B2 (en) | 2014-05-30 | 2019-09-17 | Apple Inc. | Exemplar-based natural language processing |
US10083690B2 (en) | 2014-05-30 | 2018-09-25 | Apple Inc. | Better resolution when referencing to concepts |
US10169329B2 (en) | 2014-05-30 | 2019-01-01 | Apple Inc. | Exemplar-based natural language processing |
US11133008B2 (en) | 2014-05-30 | 2021-09-28 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US10714095B2 (en) | 2014-05-30 | 2020-07-14 | Apple Inc. | Intelligent assistant for home automation |
US10699717B2 (en) | 2014-05-30 | 2020-06-30 | Apple Inc. | Intelligent assistant for home automation |
US11810562B2 (en) | 2014-05-30 | 2023-11-07 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US11699448B2 (en) | 2014-05-30 | 2023-07-11 | Apple Inc. | Intelligent assistant for home automation |
US11670289B2 (en) | 2014-05-30 | 2023-06-06 | Apple Inc. | Multi-command single utterance input method |
US9966065B2 (en) * | 2014-05-30 | 2018-05-08 | Apple Inc. | Multi-command single utterance input method |
US10878809B2 (en) | 2014-05-30 | 2020-12-29 | Apple Inc. | Multi-command single utterance input method |
US10657966B2 (en) | 2014-05-30 | 2020-05-19 | Apple Inc. | Better resolution when referencing to concepts |
US20150348551A1 (en) * | 2014-05-30 | 2015-12-03 | Apple Inc. | Multi-command single utterance input method |
US11516537B2 (en) | 2014-06-30 | 2022-11-29 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US10904611B2 (en) | 2014-06-30 | 2021-01-26 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US9668024B2 (en) | 2014-06-30 | 2017-05-30 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US11838579B2 (en) | 2014-06-30 | 2023-12-05 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US10431204B2 (en) | 2014-09-11 | 2019-10-01 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10390213B2 (en) | 2014-09-30 | 2019-08-20 | Apple Inc. | Social reminders |
US10438595B2 (en) | 2014-09-30 | 2019-10-08 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US9986419B2 (en) | 2014-09-30 | 2018-05-29 | Apple Inc. | Social reminders |
US10453443B2 (en) | 2014-09-30 | 2019-10-22 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US20210224483A1 (en) * | 2015-01-27 | 2021-07-22 | Verint Systems Ltd. | Ontology expansion using entity-association rules and abstract relations |
US11030406B2 (en) | 2015-01-27 | 2021-06-08 | Verint Systems Ltd. | Ontology expansion using entity-association rules and abstract relations |
US11663411B2 (en) * | 2015-01-27 | 2023-05-30 | Verint Systems Ltd. | Ontology expansion using entity-association rules and abstract relations |
US11231904B2 (en) | 2015-03-06 | 2022-01-25 | Apple Inc. | Reducing response latency of intelligent automated assistants |
US11842734B2 (en) | 2015-03-08 | 2023-12-12 | Apple Inc. | Virtual assistant activation |
US10529332B2 (en) | 2015-03-08 | 2020-01-07 | Apple Inc. | Virtual assistant activation |
US10311871B2 (en) | 2015-03-08 | 2019-06-04 | Apple Inc. | Competing devices responding to voice triggers |
US10930282B2 (en) | 2015-03-08 | 2021-02-23 | Apple Inc. | Competing devices responding to voice triggers |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US11087759B2 (en) | 2015-03-08 | 2021-08-10 | Apple Inc. | Virtual assistant activation |
US11468282B2 (en) | 2015-05-15 | 2022-10-11 | Apple Inc. | Virtual assistant in a communication session |
US11127397B2 (en) | 2015-05-27 | 2021-09-21 | Apple Inc. | Device voice control |
US11070949B2 (en) | 2015-05-27 | 2021-07-20 | Apple Inc. | Systems and methods for proactively identifying and surfacing relevant content on an electronic device with a touch-sensitive display |
US10681212B2 (en) | 2015-06-05 | 2020-06-09 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US10356243B2 (en) | 2015-06-05 | 2019-07-16 | Apple Inc. | Virtual assistant aided communication with 3rd party service in a communication session |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US11010127B2 (en) | 2015-06-29 | 2021-05-18 | Apple Inc. | Virtual assistant for media playback |
US11947873B2 (en) | 2015-06-29 | 2024-04-02 | Apple Inc. | Virtual assistant for media playback |
US20170053643A1 (en) * | 2015-08-19 | 2017-02-23 | International Business Machines Corporation | Adaptation of speech recognition |
US9911410B2 (en) * | 2015-08-19 | 2018-03-06 | International Business Machines Corporation | Adaptation of speech recognition |
US11550542B2 (en) | 2015-09-08 | 2023-01-10 | Apple Inc. | Zero latency digital assistant |
US11853536B2 (en) | 2015-09-08 | 2023-12-26 | Apple Inc. | Intelligent automated assistant in a media environment |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US11954405B2 (en) | 2015-09-08 | 2024-04-09 | Apple Inc. | Zero latency digital assistant |
US11809483B2 (en) | 2015-09-08 | 2023-11-07 | Apple Inc. | Intelligent automated assistant for media search and playback |
US11500672B2 (en) | 2015-09-08 | 2022-11-15 | Apple Inc. | Distributed personal assistant |
US11126400B2 (en) | 2015-09-08 | 2021-09-21 | Apple Inc. | Zero latency digital assistant |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US11809886B2 (en) | 2015-11-06 | 2023-11-07 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US11526368B2 (en) | 2015-11-06 | 2022-12-13 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US11886805B2 (en) | 2015-11-09 | 2024-01-30 | Apple Inc. | Unconventional virtual assistant interactions |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10354652B2 (en) | 2015-12-02 | 2019-07-16 | Apple Inc. | Applying neural network language models to weighted finite state transducers for automatic speech recognition |
US10223066B2 (en) | 2015-12-23 | 2019-03-05 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US11853647B2 (en) | 2015-12-23 | 2023-12-26 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10942703B2 (en) | 2015-12-23 | 2021-03-09 | Apple Inc. | Proactive assistance based on dialog communication between devices |
US10446143B2 (en) | 2016-03-14 | 2019-10-15 | Apple Inc. | Identification of voice inputs providing credentials |
US9934775B2 (en) | 2016-05-26 | 2018-04-03 | Apple Inc. | Unit-selection text-to-speech synthesis based on predicted concatenation parameters |
US9972304B2 (en) | 2016-06-03 | 2018-05-15 | Apple Inc. | Privacy preserving distributed evaluation framework for embedded personalized systems |
US10249300B2 (en) | 2016-06-06 | 2019-04-02 | Apple Inc. | Intelligent list reading |
US11227589B2 (en) | 2016-06-06 | 2022-01-18 | Apple Inc. | Intelligent list reading |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
US11069347B2 (en) | 2016-06-08 | 2021-07-20 | Apple Inc. | Intelligent automated assistant for media exploration |
US10354011B2 (en) | 2016-06-09 | 2019-07-16 | Apple Inc. | Intelligent automated assistant in a home environment |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
US11037565B2 (en) | 2016-06-10 | 2021-06-15 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
US11657820B2 (en) | 2016-06-10 | 2023-05-23 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10733993B2 (en) | 2016-06-10 | 2020-08-04 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10089072B2 (en) | 2016-06-11 | 2018-10-02 | Apple Inc. | Intelligent device arbitration and control |
US10269345B2 (en) | 2016-06-11 | 2019-04-23 | Apple Inc. | Intelligent task discovery |
US10297253B2 (en) | 2016-06-11 | 2019-05-21 | Apple Inc. | Application integration with a digital assistant |
US11152002B2 (en) | 2016-06-11 | 2021-10-19 | Apple Inc. | Application integration with a digital assistant |
US10580409B2 (en) | 2016-06-11 | 2020-03-03 | Apple Inc. | Application integration with a digital assistant |
US10521466B2 (en) | 2016-06-11 | 2019-12-31 | Apple Inc. | Data driven natural language event detection and classification |
US11749275B2 (en) | 2016-06-11 | 2023-09-05 | Apple Inc. | Application integration with a digital assistant |
US11809783B2 (en) | 2016-06-11 | 2023-11-07 | Apple Inc. | Intelligent device arbitration and control |
US10942702B2 (en) | 2016-06-11 | 2021-03-09 | Apple Inc. | Intelligent device arbitration and control |
US10474753B2 (en) | 2016-09-07 | 2019-11-12 | Apple Inc. | Language identification using recurrent neural networks |
US10553215B2 (en) | 2016-09-23 | 2020-02-04 | Apple Inc. | Intelligent automated assistant |
US10043516B2 (en) | 2016-09-23 | 2018-08-07 | Apple Inc. | Intelligent automated assistant |
US11281993B2 (en) | 2016-12-05 | 2022-03-22 | Apple Inc. | Model and ensemble compression for metric learning |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
US11656884B2 (en) | 2017-01-09 | 2023-05-23 | Apple Inc. | Application integration with a digital assistant |
US11204787B2 (en) | 2017-01-09 | 2021-12-21 | Apple Inc. | Application integration with a digital assistant |
US10332518B2 (en) | 2017-05-09 | 2019-06-25 | Apple Inc. | User interface for correcting recognition errors |
US10741181B2 (en) | 2017-05-09 | 2020-08-11 | Apple Inc. | User interface for correcting recognition errors |
US10417266B2 (en) | 2017-05-09 | 2019-09-17 | Apple Inc. | Context-aware ranking of intelligent response suggestions |
US11467802B2 (en) | 2017-05-11 | 2022-10-11 | Apple Inc. | Maintaining privacy of personal information |
US10847142B2 (en) | 2017-05-11 | 2020-11-24 | Apple Inc. | Maintaining privacy of personal information |
US11599331B2 (en) | 2017-05-11 | 2023-03-07 | Apple Inc. | Maintaining privacy of personal information |
US10755703B2 (en) | 2017-05-11 | 2020-08-25 | Apple Inc. | Offline personal assistant |
US10726832B2 (en) | 2017-05-11 | 2020-07-28 | Apple Inc. | Maintaining privacy of personal information |
US10395654B2 (en) | 2017-05-11 | 2019-08-27 | Apple Inc. | Text normalization based on a data-driven learning network |
US11580990B2 (en) | 2017-05-12 | 2023-02-14 | Apple Inc. | User-specific acoustic models |
US10410637B2 (en) | 2017-05-12 | 2019-09-10 | Apple Inc. | User-specific acoustic models |
US11380310B2 (en) | 2017-05-12 | 2022-07-05 | Apple Inc. | Low-latency intelligent automated assistant |
US11301477B2 (en) | 2017-05-12 | 2022-04-12 | Apple Inc. | Feedback analysis of a digital assistant |
US11837237B2 (en) | 2017-05-12 | 2023-12-05 | Apple Inc. | User-specific acoustic models |
US10791176B2 (en) | 2017-05-12 | 2020-09-29 | Apple Inc. | Synchronization and task delegation of a digital assistant |
US11862151B2 (en) | 2017-05-12 | 2024-01-02 | Apple Inc. | Low-latency intelligent automated assistant |
US11538469B2 (en) | 2017-05-12 | 2022-12-27 | Apple Inc. | Low-latency intelligent automated assistant |
US10789945B2 (en) | 2017-05-12 | 2020-09-29 | Apple Inc. | Low-latency intelligent automated assistant |
US11405466B2 (en) | 2017-05-12 | 2022-08-02 | Apple Inc. | Synchronization and task delegation of a digital assistant |
US10810274B2 (en) | 2017-05-15 | 2020-10-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
US10482874B2 (en) | 2017-05-15 | 2019-11-19 | Apple Inc. | Hierarchical belief states for digital assistants |
US11217255B2 (en) | 2017-05-16 | 2022-01-04 | Apple Inc. | Far-field extension for digital assistant services |
US11532306B2 (en) * | 2017-05-16 | 2022-12-20 | Apple Inc. | Detecting a trigger of a digital assistant |
US10748546B2 (en) | 2017-05-16 | 2020-08-18 | Apple Inc. | Digital assistant services based on device capabilities |
US10403278B2 (en) | 2017-05-16 | 2019-09-03 | Apple Inc. | Methods and systems for phonetic matching in digital assistant services |
US20210097998A1 (en) * | 2017-05-16 | 2021-04-01 | Apple Inc. | Detecting a trigger of a digital assistant |
US10311144B2 (en) | 2017-05-16 | 2019-06-04 | Apple Inc. | Emoji word sense disambiguation |
US10909171B2 (en) | 2017-05-16 | 2021-02-02 | Apple Inc. | Intelligent automated assistant for media exploration |
US10303715B2 (en) | 2017-05-16 | 2019-05-28 | Apple Inc. | Intelligent automated assistant for media exploration |
US11675829B2 (en) | 2017-05-16 | 2023-06-13 | Apple Inc. | Intelligent automated assistant for media exploration |
US10657328B2 (en) | 2017-06-02 | 2020-05-19 | Apple Inc. | Multi-task recurrent neural network architecture for efficient morphology handling in neural language modeling |
US10445429B2 (en) | 2017-09-21 | 2019-10-15 | Apple Inc. | Natural language understanding using vocabularies with compressed serialized tries |
US10755051B2 (en) | 2017-09-29 | 2020-08-25 | Apple Inc. | Rule-based natural language processing |
US10650803B2 (en) | 2017-10-10 | 2020-05-12 | International Business Machines Corporation | Mapping between speech signal and transcript |
US10636424B2 (en) | 2017-11-30 | 2020-04-28 | Apple Inc. | Multi-turn canned dialog |
US10733982B2 (en) | 2018-01-08 | 2020-08-04 | Apple Inc. | Multi-directional dialog |
US10733375B2 (en) | 2018-01-31 | 2020-08-04 | Apple Inc. | Knowledge-based framework for improving natural language understanding |
US10789959B2 (en) | 2018-03-02 | 2020-09-29 | Apple Inc. | Training speaker recognition models for digital assistants |
US10592604B2 (en) | 2018-03-12 | 2020-03-17 | Apple Inc. | Inverse text normalization for automatic speech recognition |
US11710482B2 (en) | 2018-03-26 | 2023-07-25 | Apple Inc. | Natural assistant interaction |
US10818288B2 (en) | 2018-03-26 | 2020-10-27 | Apple Inc. | Natural assistant interaction |
US10909331B2 (en) | 2018-03-30 | 2021-02-02 | Apple Inc. | Implicit identification of translation payload with neural machine translation |
US11854539B2 (en) | 2018-05-07 | 2023-12-26 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US11487364B2 (en) | 2018-05-07 | 2022-11-01 | Apple Inc. | Raise to speak |
US10928918B2 (en) | 2018-05-07 | 2021-02-23 | Apple Inc. | Raise to speak |
US11900923B2 (en) | 2018-05-07 | 2024-02-13 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US11907436B2 (en) | 2018-05-07 | 2024-02-20 | Apple Inc. | Raise to speak |
US11169616B2 (en) | 2018-05-07 | 2021-11-09 | Apple Inc. | Raise to speak |
US11145294B2 (en) | 2018-05-07 | 2021-10-12 | Apple Inc. | Intelligent automated assistant for delivering content from user experiences |
US10984780B2 (en) | 2018-05-21 | 2021-04-20 | Apple Inc. | Global semantic word embeddings using bi-directional recurrent neural networks |
US10892996B2 (en) | 2018-06-01 | 2021-01-12 | Apple Inc. | Variable latency device coordination |
US11386266B2 (en) | 2018-06-01 | 2022-07-12 | Apple Inc. | Text correction |
US11495218B2 (en) | 2018-06-01 | 2022-11-08 | Apple Inc. | Virtual assistant operation in multi-device environments |
US11630525B2 (en) | 2018-06-01 | 2023-04-18 | Apple Inc. | Attention aware virtual assistant dismissal |
US11431642B2 (en) | 2018-06-01 | 2022-08-30 | Apple Inc. | Variable latency device coordination |
US10984798B2 (en) | 2018-06-01 | 2021-04-20 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US11009970B2 (en) | 2018-06-01 | 2021-05-18 | Apple Inc. | Attention aware virtual assistant dismissal |
US11360577B2 (en) | 2018-06-01 | 2022-06-14 | Apple Inc. | Attention aware virtual assistant dismissal |
US10403283B1 (en) | 2018-06-01 | 2019-09-03 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US10720160B2 (en) | 2018-06-01 | 2020-07-21 | Apple Inc. | Voice interaction at a primary device to access call functionality of a companion device |
US10684703B2 (en) | 2018-06-01 | 2020-06-16 | Apple Inc. | Attention aware virtual assistant dismissal |
US10504518B1 (en) | 2018-06-03 | 2019-12-10 | Apple Inc. | Accelerated task performance |
US10944859B2 (en) | 2018-06-03 | 2021-03-09 | Apple Inc. | Accelerated task performance |
US10496705B1 (en) | 2018-06-03 | 2019-12-03 | Apple Inc. | Accelerated task performance |
US11010561B2 (en) | 2018-09-27 | 2021-05-18 | Apple Inc. | Sentiment prediction from textual data |
US11462215B2 (en) | 2018-09-28 | 2022-10-04 | Apple Inc. | Multi-modal inputs for voice commands |
US11170166B2 (en) | 2018-09-28 | 2021-11-09 | Apple Inc. | Neural typographical error modeling via generative adversarial networks |
US10839159B2 (en) | 2018-09-28 | 2020-11-17 | Apple Inc. | Named entity normalization in a spoken dialog system |
US11893992B2 (en) | 2018-09-28 | 2024-02-06 | Apple Inc. | Multi-modal inputs for voice commands |
US11361161B2 (en) | 2018-10-22 | 2022-06-14 | Verint Americas Inc. | Automated system and method to prioritize language model and ontology expansion and pruning |
US11475898B2 (en) | 2018-10-26 | 2022-10-18 | Apple Inc. | Low-latency multi-speaker speech recognition |
US11638059B2 (en) | 2019-01-04 | 2023-04-25 | Apple Inc. | Content playback on multiple devices |
US11348573B2 (en) | 2019-03-18 | 2022-05-31 | Apple Inc. | Multimodality in digital assistant systems |
US11783815B2 (en) | 2019-03-18 | 2023-10-10 | Apple Inc. | Multimodality in digital assistant systems |
US11769012B2 (en) | 2019-03-27 | 2023-09-26 | Verint Americas Inc. | Automated system and method to prioritize language model and ontology expansion and pruning |
US11307752B2 (en) | 2019-05-06 | 2022-04-19 | Apple Inc. | User configurable task triggers |
US11217251B2 (en) | 2019-05-06 | 2022-01-04 | Apple Inc. | Spoken notifications |
US11705130B2 (en) | 2019-05-06 | 2023-07-18 | Apple Inc. | Spoken notifications |
US11675491B2 (en) | 2019-05-06 | 2023-06-13 | Apple Inc. | User configurable task triggers |
US11423908B2 (en) | 2019-05-06 | 2022-08-23 | Apple Inc. | Interpreting spoken requests |
US11475884B2 (en) | 2019-05-06 | 2022-10-18 | Apple Inc. | Reducing digital assistant latency when a language is incorrectly determined |
US11140099B2 (en) | 2019-05-21 | 2021-10-05 | Apple Inc. | Providing message response suggestions |
US11888791B2 (en) | 2019-05-21 | 2024-01-30 | Apple Inc. | Providing message response suggestions |
US11237797B2 (en) | 2019-05-31 | 2022-02-01 | Apple Inc. | User activity shortcut suggestions |
US11289073B2 (en) | 2019-05-31 | 2022-03-29 | Apple Inc. | Device text to speech |
US11657813B2 (en) | 2019-05-31 | 2023-05-23 | Apple Inc. | Voice identification in digital assistant systems |
US11496600B2 (en) | 2019-05-31 | 2022-11-08 | Apple Inc. | Remote execution of machine-learned models |
US11360739B2 (en) | 2019-05-31 | 2022-06-14 | Apple Inc. | User activity shortcut suggestions |
US11360641B2 (en) | 2019-06-01 | 2022-06-14 | Apple Inc. | Increasing the relevance of new available information |
US11790914B2 (en) | 2019-06-01 | 2023-10-17 | Apple Inc. | Methods and user interfaces for voice-based control of electronic devices |
US20220277733A1 (en) * | 2019-08-14 | 2022-09-01 | Unify Patente Gmbh & Co. Kg | Real-time communication and collaboration system and method of monitoring objectives to be achieved by a plurality of users collaborating on a real-time communication and collaboration platform |
US11488406B2 (en) | 2019-09-25 | 2022-11-01 | Apple Inc. | Text detection using global geometry estimators |
US11914848B2 (en) | 2020-05-11 | 2024-02-27 | Apple Inc. | Providing relevant data items based on context |
US11924254B2 (en) | 2020-05-11 | 2024-03-05 | Apple Inc. | Digital assistant hardware abstraction |
US11765209B2 (en) | 2020-05-11 | 2023-09-19 | Apple Inc. | Digital assistant hardware abstraction |
US11838734B2 (en) | 2020-07-20 | 2023-12-05 | Apple Inc. | Multi-device audio adjustment coordination |
US11696060B2 (en) | 2020-07-21 | 2023-07-04 | Apple Inc. | User identification using headphones |
US11750962B2 (en) | 2020-07-21 | 2023-09-05 | Apple Inc. | User identification using headphones |
Also Published As
Publication number | Publication date |
---|---|
EP2849177A1 (en) | 2015-03-18 |
EP2849177B1 (en) | 2020-04-08 |
US11217252B2 (en) | 2022-01-04 |
US11900943B2 (en) | 2024-02-13 |
US20220122609A1 (en) | 2022-04-21 |
US20200090660A1 (en) | 2020-03-19 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11900943B2 (en) | System and method of text zoning | |
US11380333B2 (en) | System and method of diarization and labeling of audio data | |
US11037553B2 (en) | Learning-type interactive device | |
US10147418B2 (en) | System and method of automated evaluation of transcription quality | |
US11545139B2 (en) | System and method for determining the compliance of agent scripts | |
Boháč et al. | Using suprasegmental information in recognized speech punctuation completion | |
US20240144934A1 (en) | Voice Data Generation Method, Voice Data Generation Apparatus And Computer-Readable Recording Medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: VERINT SYSTEMS LTD., ISRAEL Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:ROMANO, RONI;HORESH, YAIR;DREYFUSS, JEREMIE;SIGNING DATES FROM 20130922 TO 20130930;REEL/FRAME:033917/0748 |
|
STCV | Information on status: appeal procedure |
Free format text: ON APPEAL -- AWAITING DECISION BY THE BOARD OF APPEALS |
|
STCV | Information on status: appeal procedure |
Free format text: BOARD OF APPEALS DECISION RENDERED |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- AFTER EXAMINER'S ANSWER OR BOARD OF APPEALS DECISION |