USRE45289E1 - Selective noise/channel/coding models and recognizers for automatic speech recognition - Google Patents
Selective noise/channel/coding models and recognizers for automatic speech recognition Download PDFInfo
- Publication number
- USRE45289E1 USRE45289E1 US09/978,250 US97825001A USRE45289E US RE45289 E1 USRE45289 E1 US RE45289E1 US 97825001 A US97825001 A US 97825001A US RE45289 E USRE45289 E US RE45289E
- Authority
- US
- United States
- Prior art keywords
- noise
- speech recognition
- models
- yield
- background noise
- 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.)
- Expired - Lifetime
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
- G10L15/20—Speech recognition techniques specially adapted for robustness in adverse environments, e.g. in noise, of stress induced speech
Definitions
- the present invention relates to the robust recognition of speech in noisy environments using specific noise environment models and recognizers, and more particularly, to selective noise/channel/coding models and recognizers for automatic speech recognition.
- U.S. Pat. No. 5,148,489 issued Sep. 15, 1992 to Erell et al., relates to the preprocessing of noisy speech to minimize the likelihood of errors.
- the speech is preprocessed by calculating for each vector of speech in the presence of noise an estimate of clean speech. Calculations are accomplished by what is called minimum-mean-log-spectral distance estimations using mixture models and Markov models.
- the preprocessing calculations rely on the basic assumptions that the clean speech can be modeled because the speech and noise are uncorrelated. As this basic assumption may not be true in all cases, errors may still occur.
- U.S. Pat. No. 4,933,973, issued Jun. 12, 1990 to Porter relates to the recognition of incoming speech signals in noise.
- Pre-stored templates of noise-free speech are modified to have the estimated spectral values of noise and the same signal-to-noise ratio as the incoming signal.
- the templates are compared within a processor by a recognition algorithm.
- recognition is dependent upon proper modification of the noise-free templates. If modification is incorrectly carried out, errors may still be present in the speech recognition.
- U.S. Pat. No. 4,720,802 issued Jan. 19, 1988 to Damoulakis et al., relates to a noise compensation arrangement. Speech recognition is carried out by extracting an estimate of the background noise during unknown speech input. The noise estimate is then used to modify pre-stored noiseless speech reference signals for comparison with the unknown speech input. The comparison is accomplished by averaging values and generating sets of probability density signals. Correct recognition of the unknown speech thus relies upon the proper estimation of the background noise and proper selection of the speech reference signals. Improper estimation and selection may cause errors to occur in the speech recognition.
- the present invention provides a method and an apparatus for robust speech recognition in various noisy environments.
- the speech recognition system of the present invention is capable of higher performance than currently known methods in both noisy and other environments.
- the present invention provides noise models, created to handle specific background noises, which can quickly be determined to relate to the background noise of a specific call.
- the present invention is directed to the robust recognition of speech in noisy environments using specific noise environment models and recognizers.
- models of various noise environments are created to handle specific background noises.
- a real-time system then analyzes the background noise of an incoming call, loads the appropriate noise model and performs the speech recognition task with the model.
- the background noise models themselves, are created for each set of background noise which may be used.
- Examples of the background noises to be sampled as models would be: city noise, motor vehicle noise, truck noise, airport noise, subway train noise, cellular interference noise, etc.
- the models need not only be limited to simple background noise.
- various models may model different channel conditions, different telephone microphone characteristics, various different cellular coding techniques, Internet connections, and other noises associated with the placement of a call wherein speech recognition is to be used.
- a complete set of sub-word models can be created for each characteristic by mixing different background noise characteristics.
- models can be created by recording background noise and clean speech separately and later combining the two.
- models can be created by recording speech with the various background noise environments present.
- the models can be created using signal processing of recorded speech to alter it as if it had been recorded in the noisy background.
- Determination of which model to use is determined by the speech recognition apparatus.
- a sample of the surrounding background environment from where the call is being placed is recorded.
- the system analyzes the recorded background noise. Different methods of analysis may be used. Once the appropriate noise model has been chosen on the basis of the analysis, speech recognition is performed with the model.
- the system can also constantly monitor the speech recognition function, and if it is determined that speech recognition is not at an acceptable level, the system can replace the chosen model with another.
- FIG. 1 illustrates a speech recognition apparatus for the creation, storage and use of various background noise models, according to an embodiment of the present invention.
- FIG. 2 illustrates a flow chart for determination of the proper noise model to use, according to an embodiment of the present invention.
- FIG. 3 illustrates a flow chart for robust speech recognition and, if necessary, model replacement, according to an embodiment of the present invention.
- FIG. 4 illustrates a chart of an example of the selection of an appropriate background noise model to be used in the speech recognition application, according to an embodiment of the present invention.
- FIGS. 1 to 4 show a speech recognition apparatus and method for robust speech recognition in noisy environments according to an embodiment of the present invention.
- a hidden Markov model is created to model a specific background noise. When a call is placed, background noise is recorded and analyzed to determine which Markov model is most appropriate to use. Speech recognition is then carried out using the appropriately determined model. If speech recognition is not being performed at an acceptable level, the model may be replaced by another.
- various background noises 1 , . . . , n, n+1 are recorded using known sound collection devices, such as pick-up microphones 1 , . . . , n, n+1. It is to be understood, of course, that any collection technique, whether known or heretofore to be known, may be used.
- the various background noises which can be recorded are sounds such as: city noise, traffic noise, airport noise, subway train noise, cellular interference noise, different channel characteristics noise, various different cellular coding techniques noise, Internet connection noise, etc.
- the various individual background characteristics may also be mixed in infinite variations. For example, cellular channel characteristics noise may be mixed with background traffic noise. It is to be understood, of course, that other more various background noise may also be recorded, what is to be recorded is not to be limited and that any means sufficient for the recordation and/or storage of sound may be used.
- the recorded background noise is then modeled to create hidden Markov models for use in speech recognizers. Modeling is performed in the modeling device 10 using known modeling techniques.
- the recorded background noise and pre-labeled speech data are put through algorithms which pick out phonemes creating, in essence, statistical background noise models. As described in this embodiment then, the models are thus created by recording background noise and clean speech separately and later combining the two.
- models can be created by recording speech with the various background noise environments present.
- the models can be created using signal processing of the recorded speech to alter it as if it had been recorded in the noisy background.
- the modeled background noise is then stored in an appropriate storage device 20 .
- the storage device 20 itself may be located at a central network hub, or it may be reproduced and distributed locally.
- the various stored background noise models 1 , . . . , n, n+1 are then appropriately accessed from the storage device 20 by a speech recognition unit 30 when a call is placed by the telephone user 40 .
- the present invention will work equally well with any technique of speech recognition using the background noise models.
- a call is placed by a user and received by the telephone company in steps 100 and 110 , respectively.
- the preferred embodiment described herein is in the context of the receipt a simple telephone call, the present invention will work equally well with any speech transmission technique used and thus is not to be limited to the one embodiment.
- step 120 approximately 2 seconds worth of background noise at the caller's location is recorded and/or monitored. Of course, various lengths of time may be used based upon adequate reception and other factors.
- Introductory messages, instructions or the like are then played in step 125 . While these messages are being played, the background noise recorded in step 120 is analyzed by the system in step 130 . Even while the messages are being played to the caller, the known technique of echoing cancellation may be used to record and/or monitor further background noise. In explanation, the system will effectively cancel out the messages being played in the recording and/or monitoring of the background noise.
- Signal information such as the type of signals (ANI, DNIS, SS7 signals, etc.), channel port number, or trunk line number may be used to help restrict what the background noise is, and thus what background noise model would be most suitable.
- the system may determine that a call received over a particular trunk line number may more likely than not be from India, as that trunk line number is the designated trunk for receiving calls from India.
- the location of the call may be recognized by the caller's account number, time the call is placed or other known information about the caller and/or the call. Such information could be used as a preliminary indicator of the existence and type of background noise.
- a series of questions or instructions to be posed to the caller with corresponding answers to be made by the caller may be used. These answers may then be analyzed using each model (or a pre-determined maximum number of models) to determine which models have a higher correct match percentage. For example, the system may carry on a dialog with the caller and instruct the caller to say “NS437W”, “Boston”, and “July 1st”. The system will then analyze each response using the various background noise models. The model(s) with the correct match for each response by the caller can then be used in the speech recognition application. An illustration of the above analysis method is found in FIG. 4 .
- model n would be chosen for the following speech recognition application.
- the system may either guess, use more than one model by using more than one speech recognizer, or compare parameters of the call's recorded background noise to parameters contained in each background noise model.
- the system can store that information in a database.
- a database of which background noise models are most successful in the proper analysis of the call's background noise can be created and stored. This database can later be accessed when another incoming call is received from the same location. For example, it has previously been determined, and stored in the database, that a call from a particular location should use the city noise background noise model in the speech recognition application, because that model results in the highest percentage of correct speech recognitions. Thus the most appropriate model is used.
- the system can dynamically update itself by constantly re-analyzing the call's recorded background noise to detect potential changes in the background noise environment.
- step 140 the most appropriate background noise model is selected and recalled from the storage means 20 .
- alternative background noise models may be ordered on a standby basis in case speech recognition fails with the selected model. With the most appropriate background noise model having been selected, and other models ordered on standby, the system proceeds in step 150 to the speech recognition application using the selected model.
- step 160 the selected background noise model is loaded into the speech recognition unit 30 .
- speech recognition is performed using the chosen model.
- the speech utterance by the caller can be routed to a preset recognizer with the specific model(s) needed, or the necessary model(s) may be loaded into the speech recognition means 30 .
- step 180 the correctness of the speech recognition is determined. In this manner then, constant monitoring and adjustment can take place while the call is in progress if necessary.
- Correctness of the speech recognition in step 180 may be accomplished in several ways. If more than one speech recognizer means 30 is being used, the correct recognition of the speech utterance may be determined by using a voter scheme. That is, each speech recognizer unit 30 , using a set of models with different background noise characteristics, will analyze the speech utterance. A vote determines what analysis is correct. For example, if fifty recognizers determine that “Boston” has been said by the caller, and twenty recognizers determine that “Baltimore” has been said, than the system determines in step 180 that “Boston” must be the correct speech utterance. Alternatively, or in conjunction with the above method, the system can ask the caller to validate the determined speech utterance. For example, the system can prompt the caller by asking “Is this correct?”. A determination of correctness in step 180 can thus be made on a basis of most correct validations by the user and/or lowest rejections (rejections could be set high).
- step 185 the system returns to step 160 to load a new model, perhaps the model which was previously determined in step 140 to be the next in order.
- the minimal criteria of correctness may be set at any level deemed appropriate and most often will be experimentally determined on the basis of each individual system and its own separate characteristics.
- step 180 If the determination in step 180 is that speech recognition is proceeding at an acceptable level, then the system can proceed to carry out the caller's desired functions, as shown in step 190 .
- the present invention has many advantageous uses.
- the system is able to provide robust speech recognition in a variety of noisy environments.
- the present invention works well over a gamut of different noisy environments and is thus easy to implement.
- the speech recognition system is capable of a higher performance and a lower error rate than current systems. Even when the error rate begins to approach an unacceptable level, the present system automatically corrects itself by switching to a different model(s).
Abstract
Description
Claims (42)
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US09/978,250 USRE45289E1 (en) | 1997-11-25 | 2001-10-17 | Selective noise/channel/coding models and recognizers for automatic speech recognition |
Applications Claiming Priority (2)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US08/978,527 US5970446A (en) | 1997-11-25 | 1997-11-25 | Selective noise/channel/coding models and recognizers for automatic speech recognition |
US09/978,250 USRE45289E1 (en) | 1997-11-25 | 2001-10-17 | Selective noise/channel/coding models and recognizers for automatic speech recognition |
Related Parent Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US08/978,527 Reissue US5970446A (en) | 1997-11-25 | 1997-11-25 | Selective noise/channel/coding models and recognizers for automatic speech recognition |
Publications (1)
Publication Number | Publication Date |
---|---|
USRE45289E1 true USRE45289E1 (en) | 2014-12-09 |
Family
ID=25526176
Family Applications (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US08/978,527 Ceased US5970446A (en) | 1997-11-25 | 1997-11-25 | Selective noise/channel/coding models and recognizers for automatic speech recognition |
US09/978,250 Expired - Lifetime USRE45289E1 (en) | 1997-11-25 | 2001-10-17 | Selective noise/channel/coding models and recognizers for automatic speech recognition |
Family Applications Before (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US08/978,527 Ceased US5970446A (en) | 1997-11-25 | 1997-11-25 | Selective noise/channel/coding models and recognizers for automatic speech recognition |
Country Status (1)
Country | Link |
---|---|
US (2) | US5970446A (en) |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140324428A1 (en) * | 2013-04-30 | 2014-10-30 | Ebay Inc. | System and method of improving speech recognition using context |
US20160336025A1 (en) * | 2014-05-16 | 2016-11-17 | Alphonso Inc. | Efficient apparatus and method for audio signature generation using recognition history |
US20170213549A1 (en) * | 2016-01-21 | 2017-07-27 | Ford Global Technologies, Llc | Dynamic Acoustic Model Switching to Improve Noisy Speech Recognition |
Families Citing this family (191)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6233556B1 (en) * | 1998-12-16 | 2001-05-15 | Nuance Communications | Voice processing and verification system |
US6377922B2 (en) * | 1998-12-29 | 2002-04-23 | At&T Corp. | Distributed recognition system having multiple prompt-specific and response-specific speech recognizers |
US6275800B1 (en) * | 1999-02-23 | 2001-08-14 | Motorola, Inc. | Voice recognition system and method |
US6324499B1 (en) * | 1999-03-08 | 2001-11-27 | International Business Machines Corp. | Noise recognizer for speech recognition systems |
SE521465C2 (en) * | 1999-06-07 | 2003-11-04 | Ericsson Telefon Ab L M | Mobile phone with speech recognition system containing a spectral distance calculator. |
WO2001003113A1 (en) * | 1999-07-01 | 2001-01-11 | Koninklijke Philips Electronics N.V. | Robust speech processing from noisy speech models |
JP3969908B2 (en) * | 1999-09-14 | 2007-09-05 | キヤノン株式会社 | Voice input terminal, voice recognition device, voice communication system, and voice communication method |
US6721701B1 (en) * | 1999-09-20 | 2004-04-13 | Lucent Technologies Inc. | Method and apparatus for sound discrimination |
DE10006240A1 (en) | 2000-02-11 | 2001-08-16 | Bsh Bosch Siemens Hausgeraete | Electric cooking appliance controlled by voice commands has noise correction provided automatically by speech processing device when noise source is switched on |
US8645137B2 (en) | 2000-03-16 | 2014-02-04 | Apple Inc. | Fast, language-independent method for user authentication by voice |
US6728671B1 (en) * | 2000-03-29 | 2004-04-27 | Lucent Technologies Inc. | Automatic speech recognition caller input rate control |
DE10041456A1 (en) * | 2000-08-23 | 2002-03-07 | Philips Corp Intellectual Pty | Method for controlling devices using voice signals, in particular in motor vehicles |
US7212969B1 (en) * | 2000-09-29 | 2007-05-01 | Intel Corporation | Dynamic generation of voice interface structure and voice content based upon either or both user-specific contextual information and environmental information |
US7047197B1 (en) * | 2000-09-29 | 2006-05-16 | Intel Corporation | Changing characteristics of a voice user interface |
US7451085B2 (en) * | 2000-10-13 | 2008-11-11 | At&T Intellectual Property Ii, L.P. | System and method for providing a compensated speech recognition model for speech recognition |
US7457750B2 (en) * | 2000-10-13 | 2008-11-25 | At&T Corp. | Systems and methods for dynamic re-configurable speech recognition |
JP4244514B2 (en) * | 2000-10-23 | 2009-03-25 | セイコーエプソン株式会社 | Speech recognition method and speech recognition apparatus |
US8135589B1 (en) | 2000-11-30 | 2012-03-13 | Google Inc. | Performing speech recognition over a network and using speech recognition results |
US6915262B2 (en) | 2000-11-30 | 2005-07-05 | Telesector Resources Group, Inc. | Methods and apparatus for performing speech recognition and using speech recognition results |
US6823306B2 (en) | 2000-11-30 | 2004-11-23 | Telesector Resources Group, Inc. | Methods and apparatus for generating, updating and distributing speech recognition models |
JP3912003B2 (en) * | 2000-12-12 | 2007-05-09 | 株式会社日立製作所 | Communication device |
US6876968B2 (en) * | 2001-03-08 | 2005-04-05 | Matsushita Electric Industrial Co., Ltd. | Run time synthesizer adaptation to improve intelligibility of synthesized speech |
US7209880B1 (en) * | 2001-03-20 | 2007-04-24 | At&T Corp. | Systems and methods for dynamic re-configurable speech recognition |
DE10124762B4 (en) * | 2001-05-21 | 2004-07-15 | Siemens Ag | Method for training and operating a speech recognizer and speech recognizer with noise identification |
US6996525B2 (en) * | 2001-06-15 | 2006-02-07 | Intel Corporation | Selecting one of multiple speech recognizers in a system based on performance predections resulting from experience |
US6950796B2 (en) * | 2001-11-05 | 2005-09-27 | Motorola, Inc. | Speech recognition by dynamical noise model adaptation |
US7165028B2 (en) * | 2001-12-12 | 2007-01-16 | Texas Instruments Incorporated | Method of speech recognition resistant to convolutive distortion and additive distortion |
US6772118B2 (en) * | 2002-01-04 | 2004-08-03 | General Motors Corporation | Automated speech recognition filter |
US6934364B1 (en) * | 2002-02-28 | 2005-08-23 | Hewlett-Packard Development Company, L.P. | Handset identifier using support vector machines |
AUPS102902A0 (en) * | 2002-03-13 | 2002-04-11 | Hearworks Pty Ltd | A method and system for reducing potentially harmful noise in a signal arranged to convey speech |
AU2003209821B2 (en) * | 2002-03-13 | 2006-11-16 | Hear Ip Pty Ltd | A method and system for controlling potentially harmful signals in a signal arranged to convey speech |
US7224981B2 (en) * | 2002-06-20 | 2007-05-29 | Intel Corporation | Speech recognition of mobile devices |
US7181392B2 (en) * | 2002-07-16 | 2007-02-20 | International Business Machines Corporation | Determining speech recognition accuracy |
JP4352790B2 (en) * | 2002-10-31 | 2009-10-28 | セイコーエプソン株式会社 | Acoustic model creation method, speech recognition device, and vehicle having speech recognition device |
DE10251113A1 (en) * | 2002-11-02 | 2004-05-19 | Philips Intellectual Property & Standards Gmbh | Voice recognition method, involves changing over to noise-insensitive mode and/or outputting warning signal if reception quality value falls below threshold or noise value exceeds threshold |
DE10305369B4 (en) * | 2003-02-10 | 2005-05-19 | Siemens Ag | User-adaptive method for noise modeling |
US9106526B2 (en) * | 2003-03-21 | 2015-08-11 | Hewlett-Packard Development Company, L.P. | Traversing firewalls |
JP3836815B2 (en) * | 2003-05-21 | 2006-10-25 | 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 | Speech recognition apparatus, speech recognition method, computer-executable program and storage medium for causing computer to execute speech recognition method |
KR101058003B1 (en) * | 2004-02-11 | 2011-08-19 | 삼성전자주식회사 | Noise-adaptive mobile communication terminal device and call sound synthesis method using the device |
FR2871978B1 (en) * | 2004-06-16 | 2006-09-22 | Alcatel Sa | METHOD FOR PROCESSING SOUND SIGNALS FOR A COMMUNICATION TERMINAL AND COMMUNICATION TERMINAL USING THE SAME |
KR100745976B1 (en) * | 2005-01-12 | 2007-08-06 | 삼성전자주식회사 | Method and apparatus for classifying voice and non-voice using sound model |
US8175877B2 (en) * | 2005-02-02 | 2012-05-08 | At&T Intellectual Property Ii, L.P. | Method and apparatus for predicting word accuracy in automatic speech recognition systems |
US20070041589A1 (en) * | 2005-08-17 | 2007-02-22 | Gennum Corporation | System and method for providing environmental specific noise reduction algorithms |
US8677377B2 (en) | 2005-09-08 | 2014-03-18 | Apple Inc. | Method and apparatus for building an intelligent automated assistant |
US7729911B2 (en) * | 2005-09-27 | 2010-06-01 | General Motors Llc | Speech recognition method and system |
US7872574B2 (en) * | 2006-02-01 | 2011-01-18 | Innovation Specialists, Llc | Sensory enhancement systems and methods in personal electronic devices |
JP4245617B2 (en) * | 2006-04-06 | 2009-03-25 | 株式会社東芝 | Feature amount correction apparatus, feature amount correction method, and feature amount correction program |
JP4316583B2 (en) * | 2006-04-07 | 2009-08-19 | 株式会社東芝 | Feature amount correction apparatus, feature amount correction method, and feature amount correction program |
US9318108B2 (en) | 2010-01-18 | 2016-04-19 | Apple Inc. | Intelligent automated assistant |
EP1933303B1 (en) * | 2006-12-14 | 2008-08-06 | Harman/Becker Automotive Systems GmbH | Speech dialog control based on signal pre-processing |
US8977255B2 (en) | 2007-04-03 | 2015-03-10 | Apple Inc. | Method and system for operating a multi-function portable electronic device using voice-activation |
US7983916B2 (en) * | 2007-07-03 | 2011-07-19 | General Motors Llc | Sampling rate independent speech recognition |
US9330720B2 (en) | 2008-01-03 | 2016-05-03 | Apple Inc. | Methods and apparatus for altering audio output signals |
US8468019B2 (en) * | 2008-01-31 | 2013-06-18 | Qnx Software Systems Limited | Adaptive noise modeling speech recognition system |
US8996376B2 (en) | 2008-04-05 | 2015-03-31 | Apple Inc. | Intelligent text-to-speech conversion |
US8504365B2 (en) * | 2008-04-11 | 2013-08-06 | At&T Intellectual Property I, L.P. | System and method for detecting synthetic speaker verification |
US8121837B2 (en) | 2008-04-24 | 2012-02-21 | Nuance Communications, Inc. | Adjusting a speech engine for a mobile computing device based on background noise |
US10496753B2 (en) | 2010-01-18 | 2019-12-03 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US20100030549A1 (en) | 2008-07-31 | 2010-02-04 | Lee Michael M | Mobile device having human language translation capability with positional feedback |
JP2010183289A (en) * | 2009-02-04 | 2010-08-19 | Seiko Epson Corp | Mobile terminal and management system |
US20120311585A1 (en) | 2011-06-03 | 2012-12-06 | Apple Inc. | Organizing task items that represent tasks to perform |
US10241752B2 (en) | 2011-09-30 | 2019-03-26 | Apple Inc. | Interface for a virtual digital assistant |
US9858925B2 (en) * | 2009-06-05 | 2018-01-02 | Apple Inc. | Using context information to facilitate processing of commands in a virtual assistant |
US10241644B2 (en) | 2011-06-03 | 2019-03-26 | Apple Inc. | Actionable reminder entries |
US9431006B2 (en) | 2009-07-02 | 2016-08-30 | Apple Inc. | Methods and apparatuses for automatic speech recognition |
US8731475B1 (en) * | 2009-12-30 | 2014-05-20 | Sprint Spectrum L.P. | Method and system for determining environmental characteristics of a called communication device |
US8600743B2 (en) * | 2010-01-06 | 2013-12-03 | Apple Inc. | Noise profile determination for voice-related feature |
US10705794B2 (en) | 2010-01-18 | 2020-07-07 | Apple Inc. | Automatically adapting user interfaces for hands-free interaction |
US10276170B2 (en) | 2010-01-18 | 2019-04-30 | Apple Inc. | Intelligent automated assistant |
US10553209B2 (en) | 2010-01-18 | 2020-02-04 | Apple Inc. | Systems and methods for hands-free notification summaries |
US10679605B2 (en) | 2010-01-18 | 2020-06-09 | Apple Inc. | Hands-free list-reading by intelligent automated assistant |
EP2362620A1 (en) * | 2010-02-23 | 2011-08-31 | Vodafone Holding GmbH | Method of editing a noise-database and computer device |
US8682667B2 (en) | 2010-02-25 | 2014-03-25 | Apple Inc. | User profiling for selecting user specific voice input processing information |
US8639516B2 (en) * | 2010-06-04 | 2014-01-28 | Apple Inc. | User-specific noise suppression for voice quality improvements |
US8234111B2 (en) * | 2010-06-14 | 2012-07-31 | Google Inc. | Speech and noise models for speech recognition |
US8725506B2 (en) * | 2010-06-30 | 2014-05-13 | Intel Corporation | Speech audio processing |
US8812310B2 (en) * | 2010-08-22 | 2014-08-19 | King Saud University | Environment recognition of audio input |
US9443511B2 (en) | 2011-03-04 | 2016-09-13 | Qualcomm Incorporated | System and method for recognizing environmental sound |
US9262612B2 (en) | 2011-03-21 | 2016-02-16 | Apple Inc. | Device access using voice authentication |
US9858343B2 (en) | 2011-03-31 | 2018-01-02 | Microsoft Technology Licensing Llc | Personalization of queries, conversations, and searches |
US10642934B2 (en) | 2011-03-31 | 2020-05-05 | Microsoft Technology Licensing, Llc | Augmented conversational understanding architecture |
US9244984B2 (en) | 2011-03-31 | 2016-01-26 | Microsoft Technology Licensing, Llc | Location based conversational understanding |
US20150149167A1 (en) * | 2011-03-31 | 2015-05-28 | Google Inc. | Dynamic selection among acoustic transforms |
US9298287B2 (en) | 2011-03-31 | 2016-03-29 | Microsoft Technology Licensing, Llc | Combined activation for natural user interface systems |
US9842168B2 (en) | 2011-03-31 | 2017-12-12 | Microsoft Technology Licensing, Llc | Task driven user intents |
US9760566B2 (en) | 2011-03-31 | 2017-09-12 | Microsoft Technology Licensing, Llc | Augmented conversational understanding agent to identify conversation context between two humans and taking an agent action thereof |
KR101922744B1 (en) * | 2011-03-31 | 2018-11-27 | 마이크로소프트 테크놀로지 라이센싱, 엘엘씨 | Location-based conversational understanding |
US9454962B2 (en) | 2011-05-12 | 2016-09-27 | Microsoft Technology Licensing, Llc | Sentence simplification for spoken language understanding |
US9064006B2 (en) | 2012-08-23 | 2015-06-23 | Microsoft Technology Licensing, Llc | Translating natural language utterances to keyword search queries |
US10057736B2 (en) | 2011-06-03 | 2018-08-21 | Apple Inc. | Active transport based notifications |
US8994660B2 (en) | 2011-08-29 | 2015-03-31 | Apple Inc. | Text correction processing |
US8438023B1 (en) | 2011-09-30 | 2013-05-07 | Google Inc. | Warning a user when voice input to a device is likely to fail because of background or other noise |
US8972256B2 (en) | 2011-10-17 | 2015-03-03 | Nuance Communications, Inc. | System and method for dynamic noise adaptation for robust automatic speech recognition |
US9483461B2 (en) | 2012-03-06 | 2016-11-01 | Apple Inc. | Handling speech synthesis of content for multiple languages |
US9280610B2 (en) | 2012-05-14 | 2016-03-08 | Apple Inc. | Crowd sourcing information to fulfill user requests |
US9721563B2 (en) | 2012-06-08 | 2017-08-01 | Apple Inc. | Name recognition system |
EP2867890B1 (en) * | 2012-06-28 | 2018-04-25 | Nuance Communications, Inc. | Meta-data inputs to front end processing for automatic speech recognition |
US9495129B2 (en) | 2012-06-29 | 2016-11-15 | Apple Inc. | Device, method, and user interface for voice-activated navigation and browsing of a document |
EP2893532B1 (en) * | 2012-09-03 | 2021-03-24 | Fraunhofer-Gesellschaft zur Förderung der Angewandten Forschung e.V. | Apparatus and method for providing an informed multichannel speech presence probability estimation |
US20140074466A1 (en) | 2012-09-10 | 2014-03-13 | Google Inc. | Answering questions using environmental context |
US8484017B1 (en) * | 2012-09-10 | 2013-07-09 | Google Inc. | Identifying media content |
US9547647B2 (en) | 2012-09-19 | 2017-01-17 | Apple Inc. | Voice-based media searching |
US9691377B2 (en) | 2013-07-23 | 2017-06-27 | Google Technology Holdings LLC | Method and device for voice recognition training |
US9098467B1 (en) * | 2012-12-19 | 2015-08-04 | Rawles Llc | Accepting voice commands based on user identity |
CN103065631B (en) * | 2013-01-24 | 2015-07-29 | 华为终端有限公司 | A kind of method of speech recognition, device |
CN103971680B (en) * | 2013-01-24 | 2018-06-05 | 华为终端(东莞)有限公司 | A kind of method, apparatus of speech recognition |
US9275638B2 (en) | 2013-03-12 | 2016-03-01 | Google Technology Holdings LLC | Method and apparatus for training a voice recognition model database |
US9489965B2 (en) * | 2013-03-15 | 2016-11-08 | Sri International | Method and apparatus for acoustic signal characterization |
US9208781B2 (en) * | 2013-04-05 | 2015-12-08 | International Business Machines Corporation | Adapting speech recognition acoustic models with environmental and social cues |
US9437208B2 (en) * | 2013-06-03 | 2016-09-06 | Adobe Systems Incorporated | General sound decomposition models |
US9582608B2 (en) | 2013-06-07 | 2017-02-28 | Apple Inc. | Unified ranking with entropy-weighted information for phrase-based semantic auto-completion |
WO2014197334A2 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for user-specified pronunciation of words for speech synthesis and recognition |
WO2014197336A1 (en) | 2013-06-07 | 2014-12-11 | Apple Inc. | System and method for detecting errors in interactions with a voice-based digital assistant |
WO2014197335A1 (en) | 2013-06-08 | 2014-12-11 | Apple Inc. | Interpreting and acting upon commands that involve sharing information with remote devices |
US10176167B2 (en) | 2013-06-09 | 2019-01-08 | Apple Inc. | System and method for inferring user intent from speech inputs |
CN110442699A (en) | 2013-06-09 | 2019-11-12 | 苹果公司 | Operate method, computer-readable medium, electronic equipment and the system of digital assistants |
US20150032238A1 (en) * | 2013-07-23 | 2015-01-29 | Motorola Mobility Llc | Method and Device for Audio Input Routing |
US9548047B2 (en) | 2013-07-31 | 2017-01-17 | Google Technology Holdings LLC | Method and apparatus for evaluating trigger phrase enrollment |
US9704478B1 (en) * | 2013-12-02 | 2017-07-11 | Amazon Technologies, Inc. | Audio output masking for improved automatic speech recognition |
US9466310B2 (en) * | 2013-12-20 | 2016-10-11 | Lenovo Enterprise Solutions (Singapore) Pte. Ltd. | Compensating for identifiable background content in a speech recognition device |
US9842101B2 (en) | 2014-05-30 | 2017-12-12 | Apple Inc. | Predictive conversion of language input |
EP3480811A1 (en) | 2014-05-30 | 2019-05-08 | Apple Inc. | Multi-command single utterance input method |
US9430463B2 (en) | 2014-05-30 | 2016-08-30 | Apple Inc. | Exemplar-based natural language processing |
US9785630B2 (en) | 2014-05-30 | 2017-10-10 | Apple Inc. | Text prediction using combined word N-gram and unigram language models |
US9715875B2 (en) | 2014-05-30 | 2017-07-25 | Apple Inc. | Reducing the need for manual start/end-pointing and trigger phrases |
US10078631B2 (en) | 2014-05-30 | 2018-09-18 | Apple Inc. | Entropy-guided text prediction using combined word and character n-gram language models |
US9760559B2 (en) | 2014-05-30 | 2017-09-12 | Apple Inc. | Predictive text input |
US9904851B2 (en) | 2014-06-11 | 2018-02-27 | At&T Intellectual Property I, L.P. | Exploiting visual information for enhancing audio signals via source separation and beamforming |
US9338493B2 (en) | 2014-06-30 | 2016-05-10 | Apple Inc. | Intelligent automated assistant for TV user interactions |
US10659851B2 (en) | 2014-06-30 | 2020-05-19 | Apple Inc. | Real-time digital assistant knowledge updates |
JP6118838B2 (en) * | 2014-08-21 | 2017-04-19 | 本田技研工業株式会社 | Information processing apparatus, information processing system, information processing method, and information processing program |
US10446141B2 (en) | 2014-08-28 | 2019-10-15 | Apple Inc. | Automatic speech recognition based on user feedback |
US9818400B2 (en) | 2014-09-11 | 2017-11-14 | Apple Inc. | Method and apparatus for discovering trending terms in speech requests |
US10789041B2 (en) | 2014-09-12 | 2020-09-29 | Apple Inc. | Dynamic thresholds for always listening speech trigger |
US9646609B2 (en) | 2014-09-30 | 2017-05-09 | Apple Inc. | Caching apparatus for serving phonetic pronunciations |
US10074360B2 (en) | 2014-09-30 | 2018-09-11 | Apple Inc. | Providing an indication of the suitability of speech recognition |
US9886432B2 (en) | 2014-09-30 | 2018-02-06 | Apple Inc. | Parsimonious handling of word inflection via categorical stem + suffix N-gram language models |
US9668121B2 (en) | 2014-09-30 | 2017-05-30 | Apple Inc. | Social reminders |
US10127911B2 (en) | 2014-09-30 | 2018-11-13 | Apple Inc. | Speaker identification and unsupervised speaker adaptation techniques |
US9530408B2 (en) | 2014-10-31 | 2016-12-27 | At&T Intellectual Property I, L.P. | Acoustic environment recognizer for optimal speech processing |
US10552013B2 (en) | 2014-12-02 | 2020-02-04 | Apple Inc. | Data detection |
US9865280B2 (en) | 2015-03-06 | 2018-01-09 | Apple Inc. | Structured dictation using intelligent automated assistants |
US10567477B2 (en) | 2015-03-08 | 2020-02-18 | Apple Inc. | Virtual assistant continuity |
US9886953B2 (en) | 2015-03-08 | 2018-02-06 | Apple Inc. | Virtual assistant activation |
US9721566B2 (en) | 2015-03-08 | 2017-08-01 | Apple Inc. | Competing devices responding to voice triggers |
US9899019B2 (en) | 2015-03-18 | 2018-02-20 | Apple Inc. | Systems and methods for structured stem and suffix language models |
US9842105B2 (en) | 2015-04-16 | 2017-12-12 | Apple Inc. | Parsimonious continuous-space phrase representations for natural language processing |
US10083688B2 (en) | 2015-05-27 | 2018-09-25 | Apple Inc. | Device voice control for selecting a displayed affordance |
US10127220B2 (en) | 2015-06-04 | 2018-11-13 | Apple Inc. | Language identification from short strings |
US9672821B2 (en) * | 2015-06-05 | 2017-06-06 | Apple Inc. | Robust speech recognition in the presence of echo and noise using multiple signals for discrimination |
US10101822B2 (en) | 2015-06-05 | 2018-10-16 | Apple Inc. | Language input correction |
US11025565B2 (en) | 2015-06-07 | 2021-06-01 | Apple Inc. | Personalized prediction of responses for instant messaging |
US10186254B2 (en) | 2015-06-07 | 2019-01-22 | Apple Inc. | Context-based endpoint detection |
US10255907B2 (en) | 2015-06-07 | 2019-04-09 | Apple Inc. | Automatic accent detection using acoustic models |
US10671428B2 (en) | 2015-09-08 | 2020-06-02 | Apple Inc. | Distributed personal assistant |
US10747498B2 (en) | 2015-09-08 | 2020-08-18 | Apple Inc. | Zero latency digital assistant |
US9697820B2 (en) | 2015-09-24 | 2017-07-04 | Apple Inc. | Unit-selection text-to-speech synthesis using concatenation-sensitive neural networks |
US10366158B2 (en) | 2015-09-29 | 2019-07-30 | Apple Inc. | Efficient word encoding for recurrent neural network language models |
US11010550B2 (en) | 2015-09-29 | 2021-05-18 | Apple Inc. | Unified language modeling framework for word prediction, auto-completion and auto-correction |
US11587559B2 (en) | 2015-09-30 | 2023-02-21 | Apple Inc. | Intelligent device identification |
US10691473B2 (en) | 2015-11-06 | 2020-06-23 | Apple Inc. | Intelligent automated assistant in a messaging environment |
US10049668B2 (en) | 2015-12-02 | 2018-08-14 | 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 |
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 |
US10049663B2 (en) | 2016-06-08 | 2018-08-14 | Apple, Inc. | Intelligent automated assistant for media exploration |
DK179309B1 (en) | 2016-06-09 | 2018-04-23 | Apple Inc | Intelligent automated assistant in a home environment |
US10192552B2 (en) | 2016-06-10 | 2019-01-29 | Apple Inc. | Digital assistant providing whispered speech |
US10067938B2 (en) | 2016-06-10 | 2018-09-04 | Apple Inc. | Multilingual word prediction |
US10509862B2 (en) | 2016-06-10 | 2019-12-17 | Apple Inc. | Dynamic phrase expansion of language input |
US10490187B2 (en) | 2016-06-10 | 2019-11-26 | Apple Inc. | Digital assistant providing automated status report |
US10586535B2 (en) | 2016-06-10 | 2020-03-10 | Apple Inc. | Intelligent digital assistant in a multi-tasking environment |
DK179415B1 (en) | 2016-06-11 | 2018-06-14 | Apple Inc | Intelligent device arbitration and control |
DK179049B1 (en) | 2016-06-11 | 2017-09-18 | Apple Inc | Data driven natural language event detection and classification |
DK201670540A1 (en) | 2016-06-11 | 2018-01-08 | Apple Inc | Application integration with a digital assistant |
DK179343B1 (en) | 2016-06-11 | 2018-05-14 | Apple Inc | Intelligent task discovery |
US10951720B2 (en) | 2016-10-24 | 2021-03-16 | Bank Of America Corporation | Multi-channel cognitive resource platform |
US10593346B2 (en) | 2016-12-22 | 2020-03-17 | Apple Inc. | Rank-reduced token representation for automatic speech recognition |
US10720165B2 (en) * | 2017-01-23 | 2020-07-21 | Qualcomm Incorporated | Keyword voice authentication |
DK179745B1 (en) | 2017-05-12 | 2019-05-01 | Apple Inc. | SYNCHRONIZATION AND TASK DELEGATION OF A DIGITAL ASSISTANT |
DK201770431A1 (en) | 2017-05-15 | 2018-12-20 | Apple Inc. | Optimizing dialogue policy decisions for digital assistants using implicit feedback |
JP7211419B2 (en) * | 2018-05-15 | 2023-01-24 | 日本電気株式会社 | Pattern recognition device, pattern recognition method and pattern recognition program |
US10762905B2 (en) * | 2018-07-31 | 2020-09-01 | Cirrus Logic, Inc. | Speaker verification |
CN109087659A (en) * | 2018-08-03 | 2018-12-25 | 三星电子(中国)研发中心 | Audio optimization method and apparatus |
US11114089B2 (en) * | 2018-11-19 | 2021-09-07 | International Business Machines Corporation | Customizing a voice-based interface using surrounding factors |
US20210104237A1 (en) * | 2019-10-08 | 2021-04-08 | Zebra Technologies Corporation | Method and Apparatus for Providing Modular Speech Input to Client Applications |
US11489794B2 (en) | 2019-11-04 | 2022-11-01 | Bank Of America Corporation | System for configuration and intelligent transmission of electronic communications and integrated resource processing |
FR3104797B1 (en) * | 2019-12-17 | 2022-01-07 | Renault Sas | METHOD FOR IDENTIFYING AT LEAST ONE PERSON ON BOARD A MOTOR VEHICLE BY VOICE ANALYSIS |
US11411950B2 (en) | 2020-04-28 | 2022-08-09 | Bank Of America Corporation | Electronic system for integration of communication channels and active cross-channel communication transmission |
Citations (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4610023A (en) | 1982-06-04 | 1986-09-02 | Nissan Motor Company, Limited | Speech recognition system and method for variable noise environment |
US4720802A (en) | 1983-07-26 | 1988-01-19 | Lear Siegler | Noise compensation arrangement |
US4933973A (en) | 1988-02-29 | 1990-06-12 | Itt Corporation | Apparatus and methods for the selective addition of noise to templates employed in automatic speech recognition systems |
US5148489A (en) | 1990-02-28 | 1992-09-15 | Sri International | Method for spectral estimation to improve noise robustness for speech recognition |
US5222190A (en) | 1991-06-11 | 1993-06-22 | Texas Instruments Incorporated | Apparatus and method for identifying a speech pattern |
US5386492A (en) | 1992-06-29 | 1995-01-31 | Kurzweil Applied Intelligence, Inc. | Speech recognition system utilizing vocabulary model preselection |
US5509104A (en) | 1989-05-17 | 1996-04-16 | At&T Corp. | Speech recognition employing key word modeling and non-key word modeling |
US5617509A (en) | 1995-03-29 | 1997-04-01 | Motorola, Inc. | Method, apparatus, and radio optimizing Hidden Markov Model speech recognition |
US5649055A (en) | 1993-03-26 | 1997-07-15 | Hughes Electronics | Voice activity detector for speech signals in variable background noise |
US5649057A (en) | 1989-05-17 | 1997-07-15 | Lucent Technologies Inc. | Speech recognition employing key word modeling and non-key word modeling |
US5721808A (en) | 1995-03-06 | 1998-02-24 | Nippon Telegraph And Telephone Corporation | Method for the composition of noise-resistant hidden markov models for speech recognition and speech recognizer using the same |
US5749068A (en) | 1996-03-25 | 1998-05-05 | Mitsubishi Denki Kabushiki Kaisha | Speech recognition apparatus and method in noisy circumstances |
US5749067A (en) | 1993-09-14 | 1998-05-05 | British Telecommunications Public Limited Company | Voice activity detector |
US5761639A (en) | 1989-03-13 | 1998-06-02 | Kabushiki Kaisha Toshiba | Method and apparatus for time series signal recognition with signal variation proof learning |
US5778342A (en) * | 1996-02-01 | 1998-07-07 | Dspc Israel Ltd. | Pattern recognition system and method |
US5854999A (en) | 1995-06-23 | 1998-12-29 | Nec Corporation | Method and system for speech recognition with compensation for variations in the speech environment |
US5860062A (en) | 1996-06-21 | 1999-01-12 | Matsushita Electric Industrial Co., Ltd. | Speech recognition apparatus and speech recognition method |
US6078884A (en) * | 1995-08-24 | 2000-06-20 | British Telecommunications Public Limited Company | Pattern recognition |
-
1997
- 1997-11-25 US US08/978,527 patent/US5970446A/en not_active Ceased
-
2001
- 2001-10-17 US US09/978,250 patent/USRE45289E1/en not_active Expired - Lifetime
Patent Citations (18)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4610023A (en) | 1982-06-04 | 1986-09-02 | Nissan Motor Company, Limited | Speech recognition system and method for variable noise environment |
US4720802A (en) | 1983-07-26 | 1988-01-19 | Lear Siegler | Noise compensation arrangement |
US4933973A (en) | 1988-02-29 | 1990-06-12 | Itt Corporation | Apparatus and methods for the selective addition of noise to templates employed in automatic speech recognition systems |
US5761639A (en) | 1989-03-13 | 1998-06-02 | Kabushiki Kaisha Toshiba | Method and apparatus for time series signal recognition with signal variation proof learning |
US5649057A (en) | 1989-05-17 | 1997-07-15 | Lucent Technologies Inc. | Speech recognition employing key word modeling and non-key word modeling |
US5509104A (en) | 1989-05-17 | 1996-04-16 | At&T Corp. | Speech recognition employing key word modeling and non-key word modeling |
US5148489A (en) | 1990-02-28 | 1992-09-15 | Sri International | Method for spectral estimation to improve noise robustness for speech recognition |
US5222190A (en) | 1991-06-11 | 1993-06-22 | Texas Instruments Incorporated | Apparatus and method for identifying a speech pattern |
US5386492A (en) | 1992-06-29 | 1995-01-31 | Kurzweil Applied Intelligence, Inc. | Speech recognition system utilizing vocabulary model preselection |
US5649055A (en) | 1993-03-26 | 1997-07-15 | Hughes Electronics | Voice activity detector for speech signals in variable background noise |
US5749067A (en) | 1993-09-14 | 1998-05-05 | British Telecommunications Public Limited Company | Voice activity detector |
US5721808A (en) | 1995-03-06 | 1998-02-24 | Nippon Telegraph And Telephone Corporation | Method for the composition of noise-resistant hidden markov models for speech recognition and speech recognizer using the same |
US5617509A (en) | 1995-03-29 | 1997-04-01 | Motorola, Inc. | Method, apparatus, and radio optimizing Hidden Markov Model speech recognition |
US5854999A (en) | 1995-06-23 | 1998-12-29 | Nec Corporation | Method and system for speech recognition with compensation for variations in the speech environment |
US6078884A (en) * | 1995-08-24 | 2000-06-20 | British Telecommunications Public Limited Company | Pattern recognition |
US5778342A (en) * | 1996-02-01 | 1998-07-07 | Dspc Israel Ltd. | Pattern recognition system and method |
US5749068A (en) | 1996-03-25 | 1998-05-05 | Mitsubishi Denki Kabushiki Kaisha | Speech recognition apparatus and method in noisy circumstances |
US5860062A (en) | 1996-06-21 | 1999-01-12 | Matsushita Electric Industrial Co., Ltd. | Speech recognition apparatus and speech recognition method |
Non-Patent Citations (2)
Title |
---|
ICASSP-94. 1994 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1994. Kobayashi et al., "Markov model based noise modelling and its application to noisy speech recognition using dynamical features of speech" pp. II/57-II/60, Apr. 1994. |
Proceedings., IEEE International Joint Symposia on Intelligence and Systems. Khn et al., "Robust speech reconition using noise rejection approach." pp. 325-335, May 1998. |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20140324428A1 (en) * | 2013-04-30 | 2014-10-30 | Ebay Inc. | System and method of improving speech recognition using context |
US9626963B2 (en) * | 2013-04-30 | 2017-04-18 | Paypal, Inc. | System and method of improving speech recognition using context |
US20170221477A1 (en) * | 2013-04-30 | 2017-08-03 | Paypal, Inc. | System and method of improving speech recognition using context |
US10176801B2 (en) * | 2013-04-30 | 2019-01-08 | Paypal, Inc. | System and method of improving speech recognition using context |
US20160336025A1 (en) * | 2014-05-16 | 2016-11-17 | Alphonso Inc. | Efficient apparatus and method for audio signature generation using recognition history |
US9641980B2 (en) | 2014-05-16 | 2017-05-02 | Alphonso Inc. | Apparatus and method for determining co-location of services using a device that generates an audio signal |
US9698924B2 (en) * | 2014-05-16 | 2017-07-04 | Alphonso Inc. | Efficient apparatus and method for audio signature generation using recognition history |
US9942711B2 (en) | 2014-05-16 | 2018-04-10 | Alphonso Inc. | Apparatus and method for determining co-location of services using a device that generates an audio signal |
US10278017B2 (en) | 2014-05-16 | 2019-04-30 | Alphonso, Inc | Efficient apparatus and method for audio signature generation using recognition history |
US10575126B2 (en) | 2014-05-16 | 2020-02-25 | Alphonso Inc. | Apparatus and method for determining audio and/or visual time shift |
US20170213549A1 (en) * | 2016-01-21 | 2017-07-27 | Ford Global Technologies, Llc | Dynamic Acoustic Model Switching to Improve Noisy Speech Recognition |
US10297251B2 (en) * | 2016-01-21 | 2019-05-21 | Ford Global Technologies, Llc | Vehicle having dynamic acoustic model switching to improve noisy speech recognition |
Also Published As
Publication number | Publication date |
---|---|
US5970446A (en) | 1999-10-19 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
USRE45289E1 (en) | Selective noise/channel/coding models and recognizers for automatic speech recognition | |
US10854205B2 (en) | Channel-compensated low-level features for speaker recognition | |
US8175874B2 (en) | Personalized voice activity detection | |
US6374221B1 (en) | Automatic retraining of a speech recognizer while using reliable transcripts | |
US7392188B2 (en) | System and method enabling acoustic barge-in | |
JP4546512B2 (en) | Speech recognition system using technology that implicitly adapts to the speaker | |
US6487530B1 (en) | Method for recognizing non-standard and standard speech by speaker independent and speaker dependent word models | |
US5488652A (en) | Method and apparatus for training speech recognition algorithms for directory assistance applications | |
US5812972A (en) | Adaptive decision directed speech recognition bias equalization method and apparatus | |
US7930179B1 (en) | Unsupervised speaker segmentation of multi-speaker speech data | |
US5414755A (en) | System and method for passive voice verification in a telephone network | |
EP1159737B9 (en) | Speaker recognition | |
US8000962B2 (en) | Method and system for using input signal quality in speech recognition | |
JP2768274B2 (en) | Voice recognition device | |
US20030191636A1 (en) | Adapting to adverse acoustic environment in speech processing using playback training data | |
EP2148325B1 (en) | Method for determining the presence of a wanted signal component | |
US6246980B1 (en) | Method of speech recognition | |
KR19990043998A (en) | Pattern recognition system | |
JPH096388A (en) | Voice recognition equipment | |
EP3516652B1 (en) | Channel-compensated low-level features for speaker recognition | |
US6138097A (en) | Method of learning in a speech recognition system | |
JP2001520764A (en) | Speech analysis system | |
US20020069064A1 (en) | Method and apparatus for testing user interface integrity of speech-enabled devices | |
US20080228477A1 (en) | Method and Device For Processing a Voice Signal For Robust Speech Recognition | |
EP1096474A2 (en) | Speaker verification system and method |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: AT&T CORP., NEW YORK Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:GOLDBERG, RANDY G.;ROSEN, KENNETH H.;SACHS, RICHARD M.;AND OTHERS;SIGNING DATES FROM 19971118 TO 19971124;REEL/FRAME:033526/0246 |
|
AS | Assignment |
Owner name: AT&T PROPERTIES, LLC, NEVADA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:AT&T CORP.;REEL/FRAME:038274/0841 Effective date: 20160204 Owner name: AT&T INTELLECTUAL PROPERTY II, L.P., GEORGIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:AT&T PROPERTIES, LLC;REEL/FRAME:038274/0917 Effective date: 20160204 |
|
AS | Assignment |
Owner name: NUANCE COMMUNICATIONS, INC., MASSACHUSETTS Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:AT&T INTELLECTUAL PROPERTY II, L.P.;REEL/FRAME:041498/0316 Effective date: 20161214 |