基于重组超矢量的GMM-SVM说话人辨认系统  

GMM-SVM Speaker Identification System with Recombination of GMM Super Vector

作　　者：欧国振 孙林慧[1] 薛海双 

机构地区：[1]南京邮电大学通信与信息工程学院,江苏南京210003

出　　处：《计算机技术与发展》2017年第7期51-56,共6页Computer Technology and Development

基　　金：国家自然科学基金资助项目(61271335);江苏省自然科学基金项目(BK20140891);南京邮电大学校科研基金项目(NY214038)

摘　　要：在传统的高斯混合模型-支持向量机(Gaussian Mixture Model-Support Vector Machine,GMM-SVM)说话人辨认系统中,SVM利用从GMM矢量空间中得到的超矢量(Super Vector)直接对说话人进行建模与识别,由于没有考虑到超矢量内各均值矢量之间的关联性,识别性能有限。为此,提出了基于重组超矢量构建文本无关的GMM-SVM说话人辨认系统。该系统充分利用各相邻高斯分量的均值矢量的高度关联性,保证了重组后的超矢量能充分反映说话人身份的内在细节,使得系统具有充分利用SVM处理高维小数据性能的优越特点。验证实验结果表明,与传统的GMM-SVM系统相比,重组超矢量GMM-SVM说话人辨认系统显著地缩短了系统建模的时间,同时有效地提高了说话人的辨别率。In the traditional speaker identification system with Gaussian Mixture Model-Support Vector Machine （ GMM-SVM）, SVM u- ses super vector derived from the vector space of GMM to model and identify the target speakers directly. Since the relationship between two of mean vectors among GMM super vectors has not been considered, the performance of GMM-SVM system is limited. Thus a new text-independent GMM-SVM speaker identification system with super vector has been proposed which has made full use of tremendous correlation of each mean vector of the adjacent Gaussian components. The recombination super vectors have presented more inner detail of speakers＇ identity and enable the new system to take the advantage of the characteristics of superior performance when SVM deals with the small and high dimensional data. The experimental results demonstrate that the GMM-SVM speaker identification system with recom- bination super vector has not only achieved a higher recognition rate than the traditional GMM-SVM system,but also significantly decreased identification time of speakers.

关 键 词：说话人辨认 高斯混合模型-支持向量机 超矢量重组 辨别率 建模时间 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]