关键词: 矢量量化（VQ）, 最大后验概率（MAP）自适应, 支持向量机（SVM）, 说话人识别

Abstract: The traditional Support Vector Machine（SVM） in speaker recognition has high computational complexity.To solve this problem，this paper proposes a kind of speaker recognition system based on VQ-MAP and SVM which formulates Maximum A Posteriori Vector Quantization（VQ-MAP） procedure that adapts the mean vectors only.The result is the adapted speaker model and the parameter vectors of this model are used as the support vectors of SVM for speaker recognition.According to the results of simulation using Matlab，speaker recognition system based on VQ-MAP and SVM has significantly reduced computational complexity and the training time of SVM is short and it also has high recognition rate.

Key words: Vector Quantization（VQ）, Maximum A Posteriori（MAP） adaptation, Support Vector Machine（SVM）, speaker recognition