摘要
针对传统支持向量机(SVM)在说话人识别中运算量过大的问题,提出了VQ-MAP和SVM融合的说话人识别系统。它应用仅自适应均值向量的最大后验概率矢量量化过程(VQ-MAP),来得到自适应的说话人模型,用此模型中的参数向量作为支持向量应用于SVM来进行说话人识别。用Matlab进行仿真实验,结果表明,基于VQ-MAP和SVM融合的说话人识别系统大大降低了运算量,SVM训练时间短,且具有较高的识别率。
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
出处
《计算机工程与应用》
CSCD
北大核心
2011年第13期136-138,共3页
Computer Engineering and Applications
基金
国家自然科学基金No.60961002
广西教育厅科研项目(No.200808MS008)
桂林电子科技大学博士科研启动基金(No.UF08018Y)~~
