Maximum likelihood subband polynomial regression for robust speech recognition

Authors: Yong Lü, Zhenyang Wu

Abstract:
In this paper, we propose a model adaptation algorithm based on maximum likelihood subband polynomial regression (MLSPR) for robust speech recognition. In this algorithm, the cepstral mean vectors of prior trained hidden Markov models (HMMs) are converted to the log-spectral domain by the inverse discrete cosine transform (DCT) and each log-spectral mean vector is divided into several subband vectors. The relationship between the training and testing subband vectors is approximated by a polynomial function. The polynomial coefficients are estimated from adaptation data using the expectation–maximization (EM) algorithm under the maximum likelihood (ML) criterion. The experimental results show that the proposed MLSPR algorithm is superior to both the maximum likelihood linear regression (MLLR) adaptation and maximum likelihood subband weighting (MLSW) approach. In the MLSPR adaptation, only a very small amount of adaptation data is required and therefore it is more useful for fast model adaptation. 

Keywords:
Model adaptation
Subband polynomial regression
Hidden Markov model
Robust speech recognition

Published in: Applied Acoustics (Volume 74, Issue 5, May 2013)

Publisher: Elsevier

ISSN Information: 0003-682X

Maximum likelihood subband polynomial regression for robust speech recognition

Bình luận của bạn

*

*

*

*

 Captcha

Gửi bình luận nhập lại