基于最大似然模型插值的快速说话人自适应算法

本文提出了一种新的说话人自适应算法——最大似然模型插值。其基本思想是,利用语音单元间的相关性,根据最大似然准则由一组说话人相关模型的线性组合得到测试者的说话人自适应模型。接着介绍了此插值框架下的两种具体自适应算法:均值线性插值算法和矩阵线性插值算法。实验证明上述算法有良好的收敛性,在只有3句自适应数据时便能使识别系统的性能有较大提高。

A Speaker Adaptation Algorithm Based on Matrix Linear Interpolation

A novel speaker adaptation method named maximum likelihood model interpolation (MLMI) is proposed.The basic idea of MLMI is to compute the speaker adapted (SA) model of a test speaker by a linear convex combination of a set of speaker dependent (SD) models according to maximum likelihood (ML) criterion.This method has made use of the correlation of speech units.Then,two concrete algorithms named mean linear interpolation and matrix linear interpolation respectively are given.Experiments show that 3 adaptation utterances can give a significant performance improvement.