基于改进的最大后验概率矢量量化和最小二乘支持向量机集成算法  

Integration algorithm of improved maximum a posteriori probability vector quantization and least squares support vector machine

作　　者：张俊[1] 关胜晓[1] 

机构地区：[1]中国科学技术大学信息科学技术学院,合肥230027

出　　处：《计算机应用》2015年第7期2101-2104,共4页journal of Computer Applications

摘　　要：针对目前说话人识别系统的效率问题,采用集成算法的策略,提出一种新的说话人识别系统框架。首先,考虑到传统的最大后验概率矢量量化(VQ-MAP)算法中只关注平均矢量而不考虑权重的问题,提出了改进的VQMAP算法,使用加权平均向量来代替平均向量;然后,由于支持向量机(SVM)算法相对耗时,故采用最小二乘支持向量机(LS-SVM)替代SVM算法;最后,在说话人识别系统中,利用改进的VQ-MAP算法所得参数集作为LS-SVM的训练样本。实验结果表明,基于改进的VQ-MAP和LS-SVM的集成算法,与传统的SVM算法相比,在均使用径向基函数(RBF)核函数时,对40人样本数据建模时间上减少接近40%;在阈值为1,测试语音时长为4 s时,与传统的VQ-MAP和SVM算法相比,误识率降低了1.1%,误拒率降低了2.9%,识别率提高了3.9%;在阈值为1,测试语音时长为4 s时,与传统的VQ-MAP和LS-SVM算法相比,误识率降低了3.6%,误拒率降低了2.7%,识别率提高了4.4%。结果表明,集成算法能够有效提高算法识别率,明显减少运算时间,同时降低误识率和误拒率。In view of the current efficiency problem of speaker recognition system, this paper utilized the tactics of integration algorithm to put forward a new kind of speaker recognition system framework. The traditional Maximum A posteriori Probability Vector Quantization （VQ-MAP） algorithm only focuses on the average vector regardless of weight. In order to solve this problem, this paper put forward an improved algorithm based on VQ-MAP. The algorithm used weighted average vector instead of average vector. Moreover, Support Vector Machine （SVM） algorithm costs too much time, so Least Squares Support Vector Machine （LS-SVM） was used instead of SVM. Finally, in the speaker recognition system, this paper used the parameters calculated from the improved VQ-MAP algorithm as training set of LS-SVM. The experimental results show that, the modeling time of integration algorithm based on improved VQ-MAP and LS-SVM is about 40% less than that of traditional SVM algorithm when using the Radial Basis Function （RBF） kernel function and the sample of 40 people. As the threshold value is 1 and the test speech time is 4 s, compared to the traditional VQ-MAP and SVM algorithm, the deterrent rate is reduced by 1.1%, the false rejection rate is reduced by 2.9% and the recognition rate is increased by 3.9%. As the threshold value is 1 and the test speech time is 4 s, compared to the traditional VQ-MAP and LS-SVM algorithm, the deterrent rate is reduced by 3.6%, the false rejection rate is reduced by 2.7% and the recognition rate is increased by 4.4%. The results show that the integrated algorithm can improve the recognition rate effectively and reduce the operation time significantly, meanwhile reduce the deterrent rate and the false rejection rate.

关 键 词：最大后验概率 最小二乘支持向量机 权重 平均向量 说话人识别 

分 类 号：TN391.4[电子电信—物理电子学] TP181[自动化与计算机技术—控制理论与控制工程]