A Novel Feature Eztraction Approach Based on PCA and Improved Fisher Score Applied in Speaker Verification
Support Vector Machines (SVMs) have been applied in speaker verification successfully. But they cannot easily deal with the dynamic time structure of audio data, since they are constrained to work with fixed-length vectors. In this paper, we propose a new feature extraction approach based on PCA and improved Fisher score for the sake of solving this problem existed in SVM. This new feature extraction approach can not only map the variable length sequence to a single point in fixed-dimension space, but also can improve the speed of speaker verification system. Improved Fisher score will help a lot in preserving the original information of the utterances and will finally lead to high classification accuracy. Experimental results show the superior performance of this proposed method in comparison with the main SVM system in reducing EERfrom 9.18% to 4.57%.
Ming Li Yujuan Xing Ruiling Luo
School of Computer and Communication, LanZhou University of Technology, LanZhou, 730050, China School of Computer and Communication, LanZhou University of Technology, LanZhou, 730050, China Schoo
国际会议
2008 International Conference on Audio，Language and Image Processing(2008国际声音、语言、图像过程大会)
镇江
英文
56-60
2008-07-07（万方平台首次上网日期，不代表论文的发表时间）