Feature selection for fusion of speaker verification via Maximum Kullback-Leibler Distance

摘要：

This paper proposes an optimal scheme of feature selection for the fusion technique of speaker verification by Maximum Kullback-Leibler Distance. Through the investigations of bi-feature fusion schemes by six acoustic features, the information content of each fusion scheme via the Maximum Kullback-Leibler distance are computed in turn. The advantage of this distance is to overcome the shortcoming of the asymmetry of conventional Kullback-Leibler distance. This can keep the stability and correctness of the computation of the information content. In the experimental section, NIST 2001 corpus is used for evaluation. From the computation results by a variety of fusion schemes, it is found that the fusion between MFCC and residual phase hold the most information content. It indicates this scheme is able to yield an excellent performance. To verify its correctness, the EER evaluations are conducted. From the evaluation results, the EER of the fusion between MFCC and residual phase outperforms other fusion schemes. Therefore, the Maximum Kullback-Leibler distance can be considered as an effective metric for the feature selection in the fusion of speaker verification.

关键词： Maximum Kullback-Leibler distance feature selection speaker verification

作者: Di Liu Dong-Mei Sun Zheng-Ding Qiu

作者单位: Institute of Information Science,Beijing Jiaotong University, China and Forensics and Security Labor Institute of Information Science,Beijing Jiaotong University, China

会议类型: 国际会议

会议名称: 2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)

会议地点: 北京

会议语种:英文

页码: 565-568

在线出版日期: 2010-08-24（万方平台首次上网日期，不代表论文的发表时间）