Speaker adaptation using discriminative linear regression on tim

Details Speaker adaptation using discriminative linear regression on tim Speaker adaptation using discriminative linear regression on tim

1998-03-02

2000-08-29

Hudspeth, David R.

Data processing: speech signal processing, linguistics, language

Speech signal processing

Recognition

704252, 704255, G10L 1514

Patent

active

061121754

ABSTRACT:
A method and apparatus using a combined MLLR and MCE approach to estimating the time-varying polynomial Gaussian mean functions in the trended HMM has advantageous results. This integrated approach is referred to as the minimum classification error linear regression (MCELR), which has been developed and implemented in speaker adaptation experiments using a large body of utterances from different types of speakers. Experimental results show that the adaptation of linear regression on time-varying mean parameters is always better when fewer than three adaptation tokens are used.

REFERENCES:
patent: 5835890 (1998-11-01), Matsui et al.
Ki Young Lee "Recuresive estimation on the trended HMM in speech enhancement" PP 239-242, Nov. 1996.
C. J. Leggetter et al., "Maximum Likelihood Linear Regression For Speaker Adaptation of Continuous Density Hidden Markov Models", Computer Speech and Language, vol. 9, pp. 171-185, 1995.
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A. Sankar et al., "An Experimental Study of Acoustic Adaptation Algorithms", Proceedings ICASSP, vol. 2, pp. 713-716, 1996.
J. Takahashi et al., "Minimum Classification Error-Training For a Small Amount of Data Enhanced By Vector-Field-Smoothed Bayesian Learning", Proceedings ICASSP, vol. 2, pp. 597-600, 1996.
C. Rathinavelu et al., "The Trended HMM With Discriminative Training For Phonetic Classifcation", Proceedings ICSLP, vol. 2, pp. 1049-1052, 1996.
T. Matsui et al., "A Study of Speaker Adaptation Based on Minimum Classification Error Training",EUROSPEECH '95, vol. 1, pp. 81-84, 1995.
L. Deng et al., "Speech Recognition Using Hidden Markov Models with Polynomial Regression Functions as Nonstationary States", IEEE Transactions on Speech and Audio Processing, vol. 2, No. 4, pp. 507-520, 1994.
C. M. Ayer et al., "A Discriminatively Derived Linear Transform for Improved Speech Recognition", Proc. EUROSPEECH, vol. 1, pp. 583-586, 1993.

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