自动化学报2011，Vol.37Issue(12)：1503-1513,11.DOI:10.3724/SP.J.1004.2011.01503
一种基于关系度量融合框架的说话人认证特征级融合算法
Feature Level Fusion Based on Speaker Verification via Relation Measurement Fusion Framework
摘要
Abstract
This paper investigates the possibility of the feature level fusion based on speaker verification. According to the robustness and availability of the relation measurement fusion framework, the feature level fusion based on speaker verification is established. Through comparison of performances among the feature level fusion, traditional matching-score level fusion, and unimodal algorithms, the experimental results show that the proposed method is the best. To further analyze its correctness, this paper introduces the maximum Kullback-Leibler distance of the aspect of information theory to measure the information content. This distance overcomes the shortcoming of the asymmetry by traditional Kullback-Leibler distance and improves the precision of the information content computation. And the computational results verify the effectiveness of our algorithm, indicating that the feature level fusion can hold more discriminative information than the existing matching-score level fusion to yield a better performance.关键词
说话人认证/特征级融合/最大Kullback-Leibler距离/关系度量融合框架Key words
Speaker verification/ feature level fusion/maximum Kullback-Leibler distance/relation measurement fusion引用本文复制引用
刘镝,孙冬梅,裘正定..一种基于关系度量融合框架的说话人认证特征级融合算法[J].自动化学报,2011,37(12):1503-1513,11.基金项目
国家自然科学基金(60773015),北京市自然科学基金(4102051),中央高校基本科研业务赞专项资金(2009JBZ006)资助 （60773015）
