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Title: US5276766: Fast algorithm for deriving acoustic prototypes for automatic speech recognition
[ Derwent Title ]


Country: US United States of America

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10 pages

 
Inventor: Bahl, Lalit R.; Amawalk, NY
Bellegarda, Jerome R.; Goldens Bridge, NY
DeSouza, Peter V.; Mahopac Falls, NY
Nahamoo, David; White Plains, NY
Picheny, Michael A.; White Plains, 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: 1994-01-04 / 1991-07-16

Application Number: US1991000730714

IPC Code: Advanced: G10L 15/02; G10L 15/06; G10L 19/00;
IPC-7: G10L 9/04;

ECLA Code: G10L15/063;

U.S. Class: Current: 704/256.4; 704/E15.008;
Original: 395/002.65;

Field of Search: 381/041-45 395/2.65

Priority Number:
1991-07-16  US1991000730714

Abstract:     An apparatus for generating a set of acoustic prototype signals for encoding speech includes a memory for storing a training script model comprising a series of word-segment models. Each word-segment model comprises a series of elementary models. An acoustic measure is provided for measuring the value of at least one feature of an utterance of the training script during each of a series of time intervals to produce a series of feature vector signals representing the feature values of the utterance. An acoustic matcher is provided for estimating at least one path through the training script model which would produce the entire series of measured feature vector signals. From the estimated path, the elementary model in the training script model which would produce each feature vector signal is estimated. The apparatus further comprises a cluster processor for clustering the feature vector signals into a plurality of clusters. Each feature vector signal in a cluster corresponds to a single elementary model in a single location in a single word-segment model. Each cluster signal has a cluster value equal to an average of the feature values of all feature vectors in the signal. Finally, the apparatus includes a memory for storing a plurality of prototype vector signals. Each prototype vector signal corresponds to an elementary model, has an identifier, and comprises at least two partition values. The partition values are equal to combinations of the cluster values of one or more cluster signals corresponding to the elementary model.

Attorney, Agent or Firm: Schechter, Marc D. ;

Primary / Asst. Examiners: Fleming, Michael R.; Doerrler, Michelle

Maintenance Status: E3 Expired  Check current status

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Designated Country: DE FR GB IT 

Family: Show 13 known family members

First Claim:
Show all 14 claims
We claim:     1. An apparatus for generating a set of acoustic prototype signals for encoding speech, said apparatus comprising:
  • means for storing a model of a training script, said training script model comprising a series of word-segment models, each word-segment model being selected from a finite set of word-segment models, each word-segment model comprising a series of elementary models, each elementary model having a location in each word-segment model, each elementary model being selected from a finite set of elementary models;
  • means for measuring the value of at least one feature of an utterance of the training script during each of a series of time intervals spanned by the utterance of the training script to produce a series of feature vector signals, each feature vector signal having a feature value representing the value of the at least one feature of the utterance during a corresponding time interval;
  • means for estimating at least one path through the training script model which would produce the entire series of measured feature vector signals so as to estimate, for each feature vector signal, the corresponding elementary model in the training script model which would produce that feature vector signal;
  • means for clustering the feature vector signals into a plurality of clusters to form a plurality of cluster signals, each feature vector signal in a cluster corresponding to a single elementary model in a single location in a single word-segment model, each cluster signal having a cluster value equal to an average of the feature values of all of the feature vector signals in the cluster;
  • means for storing a plurality of prototype vector signals, each prototype vector signal corresponding to an elementary model, each prototype vector signal having an identifier and comprising at least two partition values, at least one partition value being equal to a combination of the cluster values of one or more cluster signals corresponding to the elementary model, at least one other partition value being equal to a combination of the cluster values of one or more other cluster signals corresponding to the elementary model.


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Forward References: Show 9 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (9)   |   Backward references (6)   |   Citation Link

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Patent  Pub.Date  Inventor Assignee   Title
Get PDF - 20pp US4587670  1986-05 Levinson et al.  AT&T Bell Laboratories Hidden Markov model speech recognition arrangement
Get PDF - 13pp US4741036  1988-04 Bahl et al.  International Business Machines Corporation Determination of phone weights for markov models in a speech recognition system
Get PDF - 35pp US4759068  1988-07 Bahl et al.  International Business Machines Corporation Constructing Markov models of words from multiple utterances
Get PDF - 30pp US4903305  1990-02 Gillick et al.  Dragon Systems, Inc. Method for representing word models for use in speech recognition
Get PDF - 16pp US5129001  1992-07 Bahl et al.  International Business Machines Corporation Method and apparatus for modeling words with multi-arc markov models
Get PDF - 27pp US5182773  1993-01 Bahl et al.  International Business Machines Corporation Speaker-independent label coding apparatus
       
Foreign References: None

Other Abstract Info: DERABS G93-019451

Other References:
  • Bahl, L. R., et al., "A Maximum Likelihood Approach to Continuous Speech Recognition" IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-5, No. 2, pp. 179-190, Mar. 1983. (12 pages) Cited by 42 patents
  • Jelinek, R., "Continuous Speech Recognition By Statistical Methods", Proceedsing of the IEEE, vol. 64, No. 4, pp. 532-556, Apr. 1976. (25 pages) Cited by 30 patents
  • Jelink, F. "The Development of An Experimental Discrete Dictation Recognizer" Proceedings of the IEEE, vol. 73, No. 11, Nov., 1985, pp. 1616-1624.
  • Bahl, L. R., et al., "Acoustic Markov Models Used in the Tangora Speech Recognition System" Proceedings 1988 International Conference on Acoustics, Speech and Signal Processing, New York, N.Y., Apr. 1988, pp. 497-500.
  • Nadas, A., et al., "Continuous Speech Recognition With Automatically Selected Prototypes Obtained By Either Bootstrapping Or Clustering" Proceedings 1981 International Conference on Acoustics, Speech, and Signal Processing, Atlanta, Ga., Apr. 1981, pp. 1153-1155.


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