基于最大似然线性回归的随机段模型说话人自适应研究

Research of speaker adaptation of stochastic segment models using maximum likelihood linear regression

提出了一种随机段模型系统的说话人自适应方法。根据随机段模型的模型特性,将最大似然线性回归方法引入到随机段模型系统中。在"863-test"测试集上进行的汉语连续语音识别实验显示,在不同的解码速度下,说话人自适应后汉字错误率均有明显的下降。实验结果表明,最大似然线性回归方法在随机段模型系统中同样能取得较好的效果。

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 ＂ 863test＂ test suite shows that the proposed method makes the error rate of Chinese characters decrease obvi ously under different decoding speeds.Experiment results indicate that the proposal can also improve the recognition performance on the SSM-based systems.