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Title: US6493667: Enhanced likelihood computation using regression in a speech recognition system
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Country: US United States of America

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7 pages

 
Inventor: de Souza, Peter V.; San Jose, CA
Gao, Yuqing; Mount Kisco, NY
Picheny, Michael; White Plains, NY
Ramabhadran, Bhuvana; Mount Kisco, 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: 2002-12-10 / 1999-08-05

Application Number: US1999000368669

IPC Code: Advanced: G10L 15/14; G10L 15/00;
IPC-7: G10L 15/14;

ECLA Code: G10L15/14M1; S10L15/08P;

U.S. Class: 704/240;

Field of Search: 704/240,256,219,236

Priority Number:
1999-08-05  US1999000368669

Abstract:     In order to achieve low error rates in a speech recognition system, for example, in a system employing rank-based decoding, we discriminate the most confusable incorrect leaves from the correct leaf by lowering their ranks. That is, we increase the likelihood of the correct leaf of a frame, while decreasing the likelihoods of the confusable leaves. In order to do this, we use the auxiliary information from the prediction of the neighboring frames to augment the likelihood computation of the current frame. We then use the residual errors in the predictions of neighboring frames to discriminate between the correct (best) and incorrect leaves of a given frame. We present a new methodology that incorporates prediction error likelihoods into the overall likelihood computation to improve the rank position of the correct leaf.

Attorney, Agent or Firm: Otterstedt, Paul J.Ryan, Mason & Lewis, LLP ;

Primary / Asst. Examiners: Chawan, Vijay; Storm, Donald L.

Maintenance Status: E1 Expired  Check current status
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Family: None

First Claim:
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What is claimed is:     1. A method for use with a speech recognition system in processing a current frame of a speech signal, the method comprising the steps of:
  • computing a likelihood value for the current frame of the speech signal;
  • computing a likelihood value for at least one neighboring frame, the likelihood value of the neighboring frame including a likelihood value for at least one frame preceding and a likelihood value for at least one frame succeeding the current frame of the speech signal; and
  • combining the likelihood values for the current and neighboring frames to form a final likelihood value for assignment in association with the current frame of the speech signal, wherein at least one of the likelihood values is assigned a corresponding weight before being combined.


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Forward References: Show 6 U.S. patent(s) that reference this one

       
U.S. References: Go to Result Set: All U.S. references   |  Forward references (6)   |   Backward references (4)   |   Citation Link

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Patent  Pub.Date  Inventor Assignee   Title
Get PDF - 24pp US4489435  1984-12 Moshier  Exxon Corporation Method and apparatus for continuous word string recognition
Get PDF - 48pp US4803729  1989-02 Baker  Dragon Systems, Inc. Speech recognition method
Get PDF - 14pp US5450523  1995-09 Zhao  Matsushita Electric Industrial Co., Ltd. Training module for estimating mixture Gaussian densities for speech unit models in speech recognition systems
Get PDF - 13pp US6330536  2001-12 Parthasarathy et al.  AT&T Corp. Method and apparatus for speaker identification using mixture discriminant analysis to develop speaker models
       
Foreign References: None

Other References:
  • Kenny, Patrick, Matthew Lennig, and Paul Mermelstein, "A Linear Predictive HMM for Vector-Valued Observations with Applications to Speech Recognition," IEEE Trans. Acoust. Speech. and Sig. Proc., vol. 38, No. 2, Feb. 1990, pp. 220-225.* (6 pages)
  • Smith, F. J., J. Ming, P. O'Boyle, and A. D. Irvine, "A Hidden Markov Model with Optimized Inter-Frame Dependence," 1995 Int. Conf on Acoust. Speech and Sig. Proc. ICASSP-95, vol. 1, May 9-12, 1995, pp. 209-212.*
  • Bahl et al., "Robust Methods For Using Context-Dependent Features and Models in a Continuous Speech Recognizer," ICASSP, vol. 1, pp. 533-536, 1994.
  • P.F. Brown, "The Acoustic-Modeling Problem in Automatic Speech Recognition," Ph.D. thesis, IBM RC 12750, pp. 56-62, 111-113, 1987.


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