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Title: US6260014: Specific task composite acoustic models
[ Derwent Title ]


Country: US United States of America

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7 pages

 
Inventor: Bahl, Lalit Rai; Port Jefferson, NY
Lubensky, David; Danbury, CT
Padmanabhan, Mukund; Ossining, NY
Roukos, Salim; Scarsdale, NY

Assignee: International Business Machines Corporation, Armonk, NY
other patents from INTERNATIONAL BUSINESS MACHINES CORPORATION (280070) (approx. 44,393)
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Published / Filed: 2001-07-10 / 1998-09-14

Application Number: US1998000153222

IPC Code: Advanced: G10L 15/26; G10L 15/22;
IPC-7: G10L 15/04;

ECLA Code: G10L15/26C; S10L15/22E; S10L15/22N2;

U.S. Class: Current: 704/254; 704/231; 704/255; 704/E15.044;
Original: 704/254; 704/255; 704/231;

Field of Search: 704/255,231,254

Priority Number:
1998-09-14  US1998000153222

Abstract:     A method for recognizing speech includes the steps of providing a generic model having a baseform representation of a vocabulary of words, identifying a subset of words relating to an application, constructing a task specific model for the subset of words, constructing a composite model by combining the generic and task specific models and modifying the baseform representation of the subset of words such that the subset of words are recognized by the task specific model. A system for recognizing speech includes a composite model having a generic model having a generic baseform representation of a vocabulary of words and a task specific model for recognizing a subset of words relating to an application wherein the subset of words are recognized using a modified baseform representation. A recognizer compares words input thereto with the generic model for words other than the subset of words and with the task specific model for the subset of words.

Attorney, Agent or Firm: F. Chau & Associates, LLP ;

Primary / Asst. Examiners: Tsang, Fan; Opsasnick, Michael N.

INPADOC Legal Status: Show legal status actions

Family: None

First Claim:
Show all 27 claims
What is claimed is:     1. A method for recognizing speech comprising the steps of:
  • providing a generic model having a generic phonetic baseform representation of a vocabulary of words;
  • identifying a subset of words relating to an application;
  • constructing a task specific model for the subset of words;
  • constructing a composite model by combining the generic model and the task specific model; and
  • modifying the generic phonetic baseform representation of the subset of words such that the task specific model is used to recognize the subset of words.


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Forward References: Show 17 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (17)   |   Backward references (12)   |   Citation Link

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Patent  Pub.Date  Inventor Assignee   Title
Get PDF - 7pp US5819221  1998-10 Kondo et al.  Texas Instruments Incorporated Speech recognition using clustered between word and/or phrase coarticulation
Get PDF - 12pp US5825978  1998-10 Digalakis et al.  SRI International Method and apparatus for speech recognition using optimized partial mixture tying of HMM state functions
Get PDF - 8pp US5875426  1999-02 Bahl et al.  International Business Machines Corporation Recognizing speech having word liaisons by adding a phoneme to reference word models
Get PDF - 9pp US5953701  1999-09 Neti et al.  International Business Machines Corporation Speech recognition models combining gender-dependent and gender-independent phone states and using phonetic-context-dependence
Get PDF - 23pp US5963903  1999-10 Hon et al.  Microsoft Corporation Method and system for dynamically adjusted training for speech recognition
Get PDF - 8pp US5995931  1999-11 Bahl et al.  International Business Machines Corporation Method for modeling and recognizing speech including word liaisons
Get PDF - 29pp US6029124  2000-02 Gillick et al.  Dragon Systems, Inc. Sequential, nonparametric speech recognition and speaker identification
Get PDF - 11pp US6061653  2000-05 Fisher et al.  Alcatel USA Sourcing, L.P. Speech recognition system using shared speech models for multiple recognition processes
Get PDF - 12pp US6067517  2000-05 Bahl et al.  International Business Machines Corporation Transcription of speech data with segments from acoustically dissimilar environments
Get PDF - 20pp US6070139  2000-05 Miyazawa et al.  Seiko Epson Corporation Bifurcated speaker specific and non-speaker specific speech recognition method and apparatus
Get PDF - 12pp US6073096  2000-06 Gao et al.  International Business Machines Corporation Speaker adaptation system and method based on class-specific pre-clustering training speakers
Get PDF - 20pp US6076056  2000-06 Huang et al.  Microsoft Corporation Speech recognition system for recognizing continuous and isolated speech
       
Foreign References: None

Other References:
  • Bahl et al., "Maximum Mutual Information Estimation of Hidden Markov Model Parameters for Speech Recognition," Proceedings of the ICASSP, pp. 49-52, IEEE 1986.
  • Juang et al., "Minimum Classification Error Rate Methods for Speech Recognition," IEEE Transactions on Speech and Audio Processing, vol. 5, No. 3, pp. 257-265, May 1997. (9 pages) Cited by 4 patents [ISI abstract]
  • Dempster et al., "Maximum Likelihood from Incomplete Data via the EM Algorithm," Journal of the Royal Statistical Society (B), No. 1, pp. 1-22, 1977. (38 pages) Cited by 53 patents
  • Bahl et al., "A New Algorithm for the Estimation of Hidden Markov Model Parameters" IEEE, pp. 493-496, 1988.
  • Viterbi, "Error Bounds for Convolutional Codes and an Asymptotically Optimal Decoding Algorithm," IEEE Trans. Information theory, vol. IT-13, pp. 260-269, Apr. 1967.


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