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Title: US4829577: Speech recognition method
[ Derwent Title ]


Country: US United States of America

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14 pages

 
Inventor: Kuroda, Akihiro; Tokyo, Japan
Nishimura, Masafumi; Yokohama, Japan
Sugawara, Kazuhide; Tokyo, Japan

Assignee: International Business Machines Corporation, Armonk, NY
other patents from INTERNATIONAL BUSINESS MACHINES CORPORATION (280070) (approx. 44,393)
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Published / Filed: 1989-05-09 / 1987-03-12

Application Number: US1987000025257

IPC Code: Advanced: G10L 11/00; G10L 15/06; G10L 15/14;
IPC-7: G10L 1/00; G10L 5/00;

ECLA Code: G10L15/07; G10L15/14M1;

U.S. Class: Current: 704/244; 704/256; 704/E15.011; 704/E15.029;
Original: 381/045; 381/043;

Field of Search: 381/045,43,42 364/513.5

Priority Number:
1986-03-25  JP1986000065030

Abstract: Speaker adaptation which enables a person to use a Hidden Markov model type recognizer previously trained by another person or persons. During initial training, parameters of Markov models are calculated iteratively by, for example, using the Forward-Backward algorithm. Adapting the recognizer to a new speaker involves (a) storing and utilizing intermediate results or probabilistic frequencies of a last iteration of training parameters, and (b) calculating new parameters by computing a weighted sum of the probabilistic frequencies stored during training and frequencies obtained from adaptation data derived from known utterances of words made by the new speaker.

Attorney, Agent or Firm: Arnold, Jack M. ;

Primary / Asst. Examiners: Harkcom, Gary V.; Lynt, Christopher H.

Maintenance Status: E3 Expired  Check current status

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Designated Country: DE FR GB IT 

Family: Show 8 known family members

First Claim:
Show all 4 claims
Having thus described our invention, what we claim as new, and desire to secure by Letters Patent is:     1. A speech recognition method based on recognizing words, comprising the steps of:
  • defining, for each word, a probabilistic model including (i) a plurality of states, (ii) at least one transition, each transition extending from a state to a state, (iii) a plurality of generated labels indicative of time between states, and (iv) probabilities of outputting each label in each of said transitions;
  • generating a first label string of said labels for each of said words from initial data thereof;
  • for each of said words, iteratively updating the probabilities of the corresponding probabilistic model, comprising the steps of:
    • (a) inputting a first label string into a corresponding probabilistic model;
    • (b) obtaining a first frequency of each of said labels being output at each of said transitions over the time in which the corresponding first label string is input into the corresponding probabilistic model;
    • (c) obtaining a second frequency of each of said states occurring over the time in which the corresponding first label string is inputted into the corresponding probabilistic model; and
    • (d) obtaining each of a plurality of new probabilities of said corresponding probabilistic model by dividing the corresponding first frequency by the corresponding second frequency;
  • storing the first and second frequencies obtained in the last step of said iterative updating;
  • determining which of said words require adaptation to recognize different speakers or the same speaker at different times;
  • generating, for each of said words requiring adaptation, a second label string from adaptation data comprising the probabilistic model of the word to be adapted;
  • obtaining, for each of said words requiring adaptation, a third frequency of each of said labels being outputted at each of said transitions over the time in which the corresponding second label string is inputted into the corresponding probabilistic model;
  • obtaining, for each of said words requiring adaptation, a fourth frequency of each of said states occurring over the time in which the corresponding second label string is outputted into the corresponding probabilistic model;
  • obtaining fifth frequencies by interpolation of the corresponding first and third frequencies;
  • obtaining sixth frequencies by interpolation of the corresponding second and third frequencies; and
  • obtaining adapted probabilities for said adaptation data by dividing the corresponding fifth frequency by the corresponding sixth frequency.


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Forward References: Show 47 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (47)   |   Backward references (7)   |   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 - 28pp US4593367  1986-06 Slack et al.  ITT Corporation Probabilistic learning element
Get PDF - 79pp US4718094  1988-01 Bahl et al.  International Business Machines Corp. Speech recognition system
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 - 54pp US4748670  1988-05 Bahl et al.  International Business Machines Corporation Apparatus and method for determining a likely word sequence from labels generated by an acoustic processor
Get PDF - 35pp US4759068  1988-07 Bahl et al.  International Business Machines Corporation Constructing Markov models of words from multiple utterances
Get PDF - 50pp US4783803  1988-11 Baker et al.  Dragon Systems, Inc. Speech recognition apparatus and method
       
Foreign References: None

Other Abstract Info: DERABS G87-300670

Other References:
  • "A Maximum Likelihood Approach to Continuous Speech Recognition" (IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-vol. 5, No. 2, pp. 179-190, 1983, Lalit R. Bahl, Frederick Jelinek and Robert L. Mercer). (12 pages) Cited by 42 patents
  • "Continuous Speech Recognition by Statistical Methods" (Proceedings of the IEEE-vol. 64, 1976, pp. 532-556, Frederick Jelinek). (25 pages) Cited by 30 patents
  • "An Introduction to the Application of the Theory of Probabilistic Functions of a Markov Process to Automatic Speech Recognition" (The Bell System Technical Journal-vol. 62, No. 4, 1983, pp. 1035-1074, Apr., S. E. Levinson, L. R. Rabiner and M. M. Sondhi. (40 pages) Cited by 21 patents


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