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Title: US5031217: Speech recognition system using Markov models having independent label output sets
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Country: US United States of America

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13 pages

 
Inventor: Nishimura, Masafumi; Yokohama, 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: 1991-07-09 / 1989-09-21

Application Number: US1989000411297

IPC Code: Advanced: G10L 11/00; G10L 15/02; G10L 15/10; G10L 15/14;
IPC-7: G10L 7/08;

ECLA Code: G10L15/14; T05K999/99;

U.S. Class: Current: 704/256.4; 704/243;
Original: 381/043;

Field of Search: 381/041-46 364/513.5

Priority Number:
1988-09-30  JP1988000244502

Abstract:     A speech recognition system is described that enables highly accurate speech recognition by concentrating on the transitional features of speech, without a large amount of calculation or storage of parameters. The system comprises means for generating spectrum data from input speech in every predetermined time interval; means for quantizing said spectrum data by using a predetermined spectrum prototype set for recognition, and for generating a corresponding recognition spectrum prototype identifier of each of said spectrum data; means for generating spectrum variation data from said input speech in said time interval; means for quantizing said spectrum variation data by using a predetermined spectrum variation prototype set for recognition, and for generating a corresponding recognition spectrum variation prototype identifier of each of said spectrum variation data; means for storing a plurality of established models corresponding to speech of said time interval, and identified by model identifiers relating to the spectrum data and those for the spectrum variation data, each of which models has one or more states, transitions from said states, probabilities of said transitions, output probability to output each of said recognition spectrum prototype identifiers in said states or said transitions, and output probability to output each of said recognition spectrum variation prototype identifiers in said states or said transitions; means for relating units to be recognized to a chain consisting of a plurality of probability models; means for generating a likelihood, in which a predetermined unit to be recognized outputs a stream of said recognition spectrum prototype identifiers and a stream of said recognition spectrum variation prototype identifiers generated from unknown input speech, based on said occurrence probabilities and output probabilities of the probability models related to said unit to be recognized; means for outputting the results of the recognition of said unknown input speech based on said likelihood, and means for outputting a result of recognition of said unknown input speech based on said likelihood, a plurality of said probability models having a common output probability of each recognition spectrum prototype identifier as long as said plurality of probability models have a common spectrum data related model identifier, and a plurality of said probability models having a common output probability of each recognition spectrum variation prototype identifier as long as said plurality of probability models have a common spectrum variation data related model identifier.

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

Primary / Asst. Examiners: Harkcom, Gary V.; Merecki, John A.

Maintenance Status: E2 Expired  Check current status

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Designated Country: DE FR GB IT 

Family: Show 7 known family members

First Claim:
Show all 8 claims
Having thus described my invention, what I claim as new, and desire to secure by Letters Patent:     1. A speech recognition system comprising:
  • a means for generating spectrum data from input speech in every predetermined time interval;
  • a means for quantizing said spectrum data by using a predetermined spectrum prototype set for recognition, each spectrum prototype having an identifier, and for generating a recognition spectrum prototype identifier corresponding to each of said spectrum data;
  • a means for generating spectrum variation data from said input speech in each said time interval;
  • a means for quantizing said spectrum variation data by using a predetermined spectrum variation prototype set for recognition, each spectrum variation prototype having an identifier, and for generating a recognition spectrum variation prototype identifier corresponding to each of said spectrum variation data;
  • a means for storing a plurality of probabilistic models corresponding to speech of said time interval, and identified by model identifiers relating to the spectrum data and model identifiers relating to the spectrum variation data, each of which models has one or more states, transitions from said states, probabilities of said transitions, output probabilities for outputting each of said recognition spectrum prototype identifiers at each of said states or said transitions, and output probabilities for outputting each of said recognition spectrum variation prototype identifiers at each of said states or said transitions;
  • a means for estimating, for each of a plurality of words, each word represented by a series of probabilistic models from the storage means, a likelihood that a series of spectrum prototype identifiers and a series of spectrum variation prototype identifiers generated from an utterance of the word will be the same as the spectrum prototype identifiers and spectrum variation prototype identifiers generated from the input speech; and
  • a means for outputting the word having the highest likelihood.


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Forward References: Show 66 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (66)   |   Backward references (3)   |   Citation Link

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Patent  Pub.Date  Inventor Assignee   Title
Get PDF - 33pp US4783804  1988-11 Juang et al.  American Telephone and Telegraph Company, AT&T Bell Laboratories Hidden Markov model speech recognition arrangement
Get PDF - 32pp US4827521  1989-05 Bahl et al.  International Business Machines Corporation Training of markov models used in a speech recognition system
Get PDF - 14pp US4829577  1989-05 Kuroda et al.  International Business Machines Corporation Speech recognition method
       
Foreign References: None

Other Abstract Info: DERABS G90-101268 JAPABS 140298P000052

Other References:
  • Bahl, L. R., et al "Acoustic Markov Models used in the Tangora Speech Recognition System", Proceedings of ICASSP '88, Apr. 1988, S11-3.
  • Bahl, L. R. et al "A Maximum Likelihood Approach to Continuous Speech Recognition", IEEE Trans. Pattern Analysis and Machine Intelligence, vol. PAMI-5, No. 2, pp. 179-190, 1983. (12 pages) Cited by 42 patents
  • Jelinek, F. "Continuous Speech Recognition By Statistical Methods", Proc. IEEE, vol. 64, 1976, pp. 532-556. (25 pages) Cited by 30 patents
  • Levinson, S. E., et al "An Introduction to the Application of the Theory of Probabilistic Functions of the Markov Process of Automatic Speech Recognition", The Bell System Technical Journal, vol. 64, No. 4, pp. 1035-1074, Apr. 1983. (40 pages) Cited by 21 patents


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