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Title: US4926488: Normalization of speech by adaptive labelling
[ Derwent Title ]


Country: US United States of America

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15 pages

 
Inventor: Nadas, Arthur J.; Rock Tavern, NY
Nahamoo, David; 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: 1990-05-15 / 1987-07-09

Application Number: US1987000071687

IPC Code: Advanced: G10L 11/00; G10L 15/02; G10L 15/06; G10L 15/12; G10L 15/20; G10L 19/00; G10L 21/02;
IPC-7: G10L 5/04; G10L 9/16;

ECLA Code: G10L15/07; G10L15/20;

U.S. Class: Current: 704/233; 704/E15.011; 704/E15.039;
Original: 381/041; 381/046;

Field of Search: 364/513.5 381/041-50

Priority Number:
1987-07-09  US1987000071687

Abstract: In a speech processor system in which prototype vectors of speech are generated by an acoustic processor under reference noise and known ambient conditions and in which feature vectors of speech are generated during varying noise and other ambient and recording conditions, normalized vectors are generated to reflect the form the feature vectors would have if generated under the reference conditions. The normalized vectors are generated by: (a) applying an operator function Ai to a set of feature vectors x occurring at or before time interval i to yield a normalized vector yi =Ai (x); (b) determining a distance error vector Ei by which the normalized vector is projectively moved toward the closest prototype vector to the normalized vector yi ; (c) up-dating the operator function for next time interval to correspond to the most recently determined distance error vector; and (d) incrementing i to the next time interval and repeating steps (a) through (d) wherein the feature vector corresponding to the incremented i value has the most recent up-dated operator function applied thereto. With successive time intervals, successive normalized vectors are generated based on a successively up-dated operator function. For each normalized vector, the closest prototype thereto is associated therewith. The string of normalized vectors or the string of associated prototypes (or respective label identifiers thereof) or both provide output from the acoustic processor.

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

Primary / Asst. Examiners: Harkcom, Gary V.; Knepper, David D.

Maintenance Status: E3 Expired  Check current status

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Designated Country: DE FR GB 

Family: Show 9 known family members

First Claim:
Show all 8 claims
We claim:     1. A speech coding apparatus comprising:
  • means for measuring the value of at least one feature of an utternace, said utternace occurring over a series of successive time intervals, said means measuring the feature value of the utterance during each time interval to produce a series of feature vector signals representing the feature values;
  • means for storing a plurality of prototype vector signals, each prototype vector signal having at least one parameter value and having a unique identification value;
  • means for generating a first modified feature vector signal having a modified feature value, said modified feature value being related, by a modification function, to the feature value of a first feature vector signal in the series of feature vector signals;
  • means for comparing the modified feature value of the first modified feature vector signal to the parameter values of the prototype vector signals to determine the associated prototype vector signal which is best matched to the first modified feature vector signal;
  • means for altering the modification function to improve the match between the modified feature vector signal and its associated prototype vector signal determined by the comparison;
  • means for generating a second modified feature vector signal having a modified feature value, said modified feature value of the second modified feature vector being related, by the altered modification function, to the feature value of a second feature vector signal in the series of feature vector signals, said second feature vector signal following the first feature vector signal;
  • means for comparing the modified feature value of the second modified feature vector signal to the parameter values of the prototype vector signals to determine the associated prototype vector signal which is best matched to the second modified feature vector signal; and
  • means for outputting the identification value of the prototype vector signal associated with the second modified feature vector as a coded representation of the second feature vector signal.


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Forward References: Show 24 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (24)   |   Backward references (19)   |   Citation Link

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Patent  Pub.Date  Inventor Assignee   Title
Get PDF - 11pp US2938079  1960-05 Flanagan   Spectrum segmentation system for the automatic extraction of formant frequencies from human speech
Get PDF - 14pp US3673331  1972-06 Hair et al.  Texas Instruments Incorporated IDENTITY VERIFICATION BY VOICE SIGNALS IN THE FREQUENCY DOMAIN
Get PDF - 11pp US3770891  1973-11 Kalfaian   VOICE IDENTIFICATION SYSTEM WITH NORMALIZATION FOR BOTH THE STORED AND THE INPUT VOICE SIGNALS
Get PDF - 17pp US3969698  1976-07 Bollinger et al.  International Business Machines Corporation Cluster storage apparatus for post processing error correction of a character recognition machine
Get PDF - 10pp US4227046  1980-10 Nakajima et al.  Hitachi, Ltd. Pre-processing system for speech recognition
Get PDF - 14pp US4256924  1981-03 Sakoe  Nippon Electric Co., Ltd. Device for recognizing an input pattern with approximate patterns used for reference patterns on mapping
Get PDF - 13pp US4282403  1981-08 Sakoe  Nippon Electric Co., Ltd. Pattern recognition with a warping function decided for each reference pattern by the use of feature vector components of a few channels
Get PDF - 18pp US4292471  1981-09 Kuhn et al.  U.S. Philips Corporation Method of verifying a speaker
Get PDF - 17pp US4394538  1983-07 Warren et al.  Threshold Technology, Inc. Speech recognition system and method
Get PDF - 16pp US4519094  1985-05 Brown et al.  AT&T Bell Laboratories LPC Word recognizer utilizing energy features
Get PDF - 15pp US4559604  1985-12 Ichikawa et al.  Hitachi, Ltd. Pattern recognition method
Get PDF - 16pp US4597098  1986-06 Noso et al.  Nissan Motor Company, Limited Speech recognition system in a variable noise environment
Get PDF - 13pp US4601054  1986-07 Watari et al.  Nippon Electric Co., Ltd. Pattern distance calculating equipment
Get PDF - 15pp US4658426  1987-04 Chabries et al.  Antin; Harold Adaptive noise suppressor
Get PDF - 79pp US4718094  1988-01 Bahl et al.  International Business Machines Corp. Speech recognition system
Get PDF - 27pp US4720802  1988-01 Damoulakis et al.  Lear Siegler Noise compensation arrangement
Get PDF - 19pp US4752957  1988-06 Maeda  Kabushiki Kaisha Toshiba Apparatus and method for recognizing unknown patterns
Get PDF - 17pp US4802224  1989-01 Shiraki et al.  Nippon Telegraph and Telephone Corporation Reference speech pattern generating method
Get PDF - 48pp US4803729  1989-02 Baker  Dragon Systems, Inc. Speech recognition method
       
Foreign References: None

Other Abstract Info: DERABS G89-033143

Other References:
  • Paul, "An 800 PBS Adaptive Vector Quantization Vocoder Using a Perceptual Distance Measure", ICASSP '83 Boston, pp. 73-76.
  • Burton et al., "Isolated-Word Recognition Using Multisection Vector Quantization Codebooks", IEEE Trans. on ASSP, vol. 33, No. 4, Aug. 1985, pp. 837-849. (13 pages) Cited by 4 patents
  • Technical Disclosure Bulletin, vol. 28, No. 11, Apr. 1986, pp. 5401-5402, by K. Sugawara, Entitled "Method for Making Confusion Matrix by DP Matching".
  • Shikano, K., et al., "Speaker Adaptation Through Vector Quantization", ICASSP '86, Tokyo, pp. 2643-2646.
  • Tappert, C. C., et al., "Fast Training Method for Speech Recognition Systems", IBM Tech. Discl. Bull., vol. 21, No. 8, Jan. 1979, pp. 3413-3414.
  • Technical Disclosure Bulletin, vol. 28, No. 11, Apr. 1986, pp. 5401-5402, by K. Sugawara, Entitled, "Method for Making Confusion Matrix by DP Matching".


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