Maximum likelihood polynomial regression for robust speech recognition

The linear hypothesis is the main disadvantage of maximum likelihood linear regression（MLLR）.This paper applies the polynomial regression method to model adaptation and establishes a nonlinear adaptation algorithm using maximum likelihood polynomial regression（MLPR） for robust speech recognition.In this algorithm,the nonlinear relationship between training and testing mean vectors in every Mel-band is approximated by a set of polynomials.The polynomial coefficients are estimated from small adaptation data in test environment by the expectation-maximization （EM） algorithm and maximum likelihood（ML） criterion.The experimental results show that the second-order polynomial can approximate the nonlinear function of training and testing mean vectors perfectly.In noise compensation and speaker adaptation,the word error rates of MLPR are significantly lower than those of MLLR.The proposed algorithm overcomes the limitation of linear hypothesis well and can decrease the impact of noise,speaker and other factors simultaneously. It is especially suitable for joint adaptation of speaker and noise.