Model Adaptation Algorithm Using Vector Taylor Series

Lü Yong; Wu Zhen-yang

doi:10.3724/SP.J.1146.2008.01768

Volume 32 Issue 1

Aug. 2010

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Lü Yong, Wu Zhen-yang. Model Adaptation Algorithm Using Vector Taylor Series[J]. Journal of Electronics & Information Technology, 2010, 32(1): 107-111. doi: 10.3724/SP.J.1146.2008.01768

Citation:

Lü Yong, Wu Zhen-yang. Model Adaptation Algorithm Using Vector Taylor Series[J]. Journal of Electronics & Information Technology, 2010, 32(1): 107-111. doi: 10.3724/SP.J.1146.2008.01768

Lü Yong, Wu Zhen-yang. Model Adaptation Algorithm Using Vector Taylor Series[J]. Journal of Electronics & Information Technology, 2010, 32(1): 107-111. doi: 10.3724/SP.J.1146.2008.01768

Citation:

Lü Yong, Wu Zhen-yang. Model Adaptation Algorithm Using Vector Taylor Series[J]. Journal of Electronics & Information Technology, 2010, 32(1): 107-111. doi: 10.3724/SP.J.1146.2008.01768

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Model Adaptation Algorithm Using Vector Taylor Series

doi: 10.3724/SP.J.1146.2008.01768

Received Date: 2008-12-22
Rev Recd Date: 2009-09-18
Publish Date: 2010-01-19

Abstract

Abstract

In actual environments the performance of speech recognition system may be degraded significantly because of the mismatch between the training and testing conditions. Model adaptation is an efficient approach that could reduce this mismatch, which adapts model parameters to new conditions by some adaptation data. In this paper, a new model adaptation using vector Taylor series is presented, which adapts the mean vector and covariance matrix of hidden Markov model. The experimental results show that the proposed algorithm is more effective than MLLR and the feature compensation algorithm based on vector Taylor series in various environments, especially in low signal-to-noise ratio environments.

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References

[1] Moreno P J, Raj B, and Stern R M. A vector Taylor seriesapproach for environment- independent speech recognition[C].Proc. IEEE International Conference on Acoustics, Speech,and Signal Processing (ICASSP), Atlanta, Georgia, USA,7-10 May 1996: 733-736. [2] Moreno P J. Speech recognition in noisy environments[D].[Ph.D. dissertation], Carnegie Mellon University, 1996. [3] Sasou A, Asano F, and Nakamura S, et al.. HMM-basednoise-robust feature compensation[J].Speech Communication.2006, 48(9):1100-1111 [4] Kim W and Hansen J H L. Feature compensation in thecepstral domain employing model combination[J]. SpeechCommunication, 2009, 51(2): 83-96. [5] Gauvain J L and Lee C H. Maximum a posteriori estimationfor multivariate Gaussian mixture observations of Markovchains[J].IEEE Transactions on Speech and Audio Processing.1994, 2(2):291-298 [6] Leggetter C J and Woodland P C. Maximum likelihood linearregression for speaker adaptation of continuous densityhidden Markov models[J].Computer Speech and Language.1995, 9(2):171-185 [7] Gales M J F and Woodland P C. Mean and varianceadaptation within the MLLR framework[J].Computer Speechand Language.1996, 10(4):249-264 [8] Gales M J F and Young S J. Robust speech recognition inadditive and convolutional noise using parallel modelcombination[J].Computer Speech and Language.1995, 9(4):289-307 [9] Kim D and Yook D. Linear spectral transformation for robustspeech recognition using maximum mutual information[J].IEEE Signal Processing Letters.2007, 14(7):496-499 [10] Li J, Deng L, and Yu D, et al.. High-performance HMMadaptation with joint compensation of additive andconvolutive distortions via vector Taylor series[C]. Proc.IEEE Workshop on Automatic Speech Recognition andUnderstanding (ASRU), Kyoto, Japan, 9-13 December 2007:65-70.

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