基于D-S证据理论的说话人识别方法

Speaker Recognition Based on Dempster-Shafter Evidence Theory

提出一种新的说话人识别方法,即将D-S证据理论应用于说话人识别中。该方法通过抽取说话人特征,用D-S证据理论对语音特征矢量的各个分量进行数据融合,重新分配基本概率赋值,并依此得出证据可信度,从而达到识别说话人身份的目的。仿真实验证明使用D-S证据理论对说话人的识别比使用矢量量化有更好的识别效果。

A new speaker recognition method, based on the D-S evidence theory, is proposed. D-S evidence theory is a useful method to deal with uncertainty and can combine evidences from different evidence sources. Therefore it has been successfully applied to data fusion and objection recognition. In this paper, features of the speakers are extreacted and fused with D-S approach, the basic probability assignment are assigned repeatedly. As a result, the reliability is obtained and the identification of speakers can be achieved efficiently. The experiments indicate that speaker recognition based on Dempster-Shafter evidence theory has better effect than speaker recognition based on vector quantization.