基于最大似然线性回归的随机段模型说话人自适应研究.

A speaker adaptation method of Stochastic Segment Model(SSM)is proposed. According to the SSM's characteristics, the theory of Maximum Likelihood Linear Regression(MLLR)method is introduced into the SSM-based systems. Continuous Chinese speech recognition experiment on "863-test" test suite shows that the proposed method makes the error rate of Chinese characters decrease obviously under different decoding speeds. Experiment results indicate that the proposal can also improve the recognition performance on the SSM-based systems. [ABSTRACT FROM AUTHOR]