关键词: 说话人识别, 矢量量化, 聚类特征, Mel频率倒谱系数

Abstract: In this paper,in order to solve the problem of distortion in speaker recognition with vector quantization,we propose a method in which we apply speaker feature based on speech clustering to vector quantization in speaker recognition.Before codebook training,the training samples of speakers would be clustered and filtrated.The experiment showed that it could reduce the number of codebook from 64 with simple vector quantization to 8 with VQ based on clustering features.The result showed：on the one hand,with the approach,the problem of distortion because of the lack of training samples would be solved to a certain extent,on the other hand,better recognition results would be acquired in lower number of codebook with the approach.In other word,the efficiency of speaker recognition is to be increased.

Key words: speaker recognition, vector quantization, clustering features, MFCC