SPEAKER IDENTIFICATION BASED ON EMD

摘要：

This paper proposes a novel approach which combines empirical mode decomposition (EMD), short-time analysis and support vector machine (SVM) for text-independent speaker recognition. Short-time analysis is used for the result of empirical mode decomposition to extract speech features of speakers, and then the support vector machine are used for speaker recognition. Experiments demonstrate that the proposed approach outperforms GMM based traditional methods, with the increased recognition rate from 92.5% to 95.1%.

关键词： speaker recognition empirical mode decomposition (EMD) short-time analysis support vector machine (SVM)

作者: Yali Liu Hongwu Yang Hui Zhou

作者单位: College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou 730070, China

会议类型: 国际会议

会议名称: 2009 IEEE International Conference on Network Infrastructure and Digital Content(2009年IEEE网络基础设施与数字内容国际会议 IEEE IC-NIDC2009)

会议地点: 北京

会议语种:英文

页码: 808-812

在线出版日期: 2009-11-06（万方平台首次上网日期，不代表论文的发表时间）