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Title: US5195167: Apparatus and method of grouping utterances of a phoneme into context-dependent categories based on sound-similarity for automatic speech recognition
[ Derwent Title ]


Country: US United States of America

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15 pages

 
Inventor: Bahl, Lalit R.; Amawalk, NY
De Souza, Peter V.; Yorktown Heights, NY
Gopalakrishnan, Ponani S.; Croton-on-Hudson, 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: 1993-03-16 / 1992-04-17

Application Number: US1992000871600

IPC Code: Advanced: G06F 7/38; G06F 17/27; G10L 11/00; G10L 15/06; G10L 15/10; G10L 15/18; G10L 15/02;
IPC-7: G10L 9/00; G10L 9/06;

ECLA Code: G10L15/063;

U.S. Class: Current: 704/200; 704/236; 704/242; 704/254; 704/E15.008;
Original: 395/002;

Field of Search: 395/002 381/041-43

Priority Number:
1992-04-17  US1992000871600
1990-01-23  US46854690D

Abstract:     Symbol feature values and contextual feature values of each event in a training set of events are measured. At least two pairs of complementary subsets of observed events are selected. In each pair of complementary subsets of observed events, one subset has contextual features with values in a set Cn, and the other set has contextual features with values in a set Cn, were the sets in Cn and Cn are complementary sets of contextual feature values. For each subset of observed events, the similarity values of the symbol features of the observed events in the subsets are calculated. For each pair of complementary sets of observed events, a "goodness of fit" is the sum of the symbol feature value similarity of the subsets. The sets of contextual feature values associated with the subsets of observed events having the best "goodness of fit" are identified and form context-dependent bases for grouping the observed events into two output sets.

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

Primary / Asst. Examiners: Knepper, David D.;

Maintenance Status: E2 Expired  Check current status

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Related Applications:
Application Number Filed Patent Pub. Date  Title
US1990000468546 1990-01-23       


       
Parent Case:     This is a continuation of application Ser. No. 07/468,546, filed Jan. 23, 1990, now abandoned.

Designated Country: DE FR GB 

Family: Show 7 known family members

First Claim:
Show all 10 claims
We claim:     1. A method of automatically grouping utterances of a phoneme into similar categories and correlating the groups of utterances with different contexts, said method comprising the steps of:
  • providing a training script comprising a series of phonemes, said training script comprising a plurality of occurrences of a selected phoneme, each occurrence of the selected phoneme having a context of one or more other phonemes preceding or following the selected phoneme in the training script;
  • measuring the value of an acoustic feature of an utterance of the phonemes in the training script during each of a series of time intervals to produce a series of acoustic feature vector signals representing the acoustic feature values of the utterance, each acoustic feature vector signal corresponding to an occurrence of a phoneme in the training script;
  • selecting a pair of first and second subsets of the set of occurrences of the selected phoneme in the training script, each occurrence of the selected phoneme in the first subset having a first context, each occurrence of the selected phoneme in the second subset having a second context different from the first context;
  • selecting a pair of third and fourth subsets of the set of occurrences of the selected phoneme in the training script, each occurrence of the selected phoneme in the third subset having a third context different from the first and second contexts, each occurrence of the selected phoneme in the fourth subset having a fourth context different from the first, second, and third contexts;
  • for each pair of subsets, determining the similarity of the acoustic feature values of the acoustic feature vector signals corresponding to the occurrences of the selected phoneme in one subset of the pair, and determining the similarity of the acoustic feature values of the acoustic feature vector signals corresponding to the occurrences of the selected phoneme in the other subset of the pair, the combined similarities for both subsets in the pair being a "goodness of fit" which estimates how well the contexts of the selected phoneme explain variations in the acoustic feature values of the utterances of the selected phoneme;
  • identifying first and second best contexts associated with the pair of subsets having the best "goodness of fit"; and
  • grouping the utterances of the selected phoneme into a first output set of utterances of the selected phoneme having the first best context, and grouping the utterances of the selected phoneme into a second output set of utterances of the selected phoneme having the second best context.


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Forward References: Show 30 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (30)   |   Backward references (8)   |   Citation Link

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PDF
Patent  Pub.Date  Inventor Assignee   Title
Get PDF - 16pp US4592085  1986-05 Watari et al.  Sony Corporation Speech-recognition method and apparatus for recognizing phonemes in a voice signal
Get PDF - 17pp US4661915  1987-04 Ott  Texas Instruments Incorporated Allophone vocoder
Get PDF - 10pp US4716593  1987-12 Hirai et al.  Tokyo Shibaura Denki Kabushiki Kaisha Identity verification system
Get PDF - 35pp US4759068  1988-07 Bahl et al.  International Business Machines Corporation Constructing Markov models of words from multiple utterances
Get PDF - 35pp US4799261  1989-01 Lin et al.  Texas Instruments Incorporated Low data rate speech encoding employing syllable duration patterns
Get PDF - 15pp US4829572  1989-05 Kong  Chung; Andrew Ho Speech recognition system
Get PDF - 53pp US4833712  1989-05 Bahl et al.  International Business Machines Corporation Automatic generation of simple Markov model stunted baseforms for words in a vocabulary
Get PDF - 10pp US4975959  1990-12 Benbassat  Texas Instruments Incorporated Speaker independent speech recognition process
       
Foreign References: None

Other Abstract Info: DERABS G91-116870 DERABS G91-224217

Other References:
  • Wilpon, "A Modified K-Means Clustering Algorithm for Use in Isolated Word Recognition," IEEE Trans. on ASSP, vol. ASSP-33, No. 3, Jun. 1985, pp. 587-594. (8 pages) Cited by 7 patents
  • Levinson et al, "Interactive Clustering Techniques for Selecting Speaker-Independent Reference Templates for Isolated Word Recognition", IEEE Trans. on ASSP, vol. ASSP-27, No. 2, Apr. 1979.
  • Rabiner et al., "Speaker-Independent Recognition of Isolated Words Using Clustering Techniques", IEEE Trans. on ASSP, vol. ASSP-27, No. 4, Aug. 1979, pp. 339-349.
  • Bahl, L. R. et al. "Acoustic Markov Models Used In The Tangora Speech Recognition System," Proc. 1988 IEEE ICASSP, pp. 497-500, 1988.


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