Speaker verification based on SVM and total variability
GUO Wu1, ZHANG Sheng1, XU Jie2, HU Guoping3, MA Xiaokong1
1. Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230026, China;
2. National Computer Network Emergency Response Technical Team Coordination Center of China, Beijing 100029, China;
3. IFLYTEK Corporation, Hefei 230088, China
Abstract：The total variability factor extractor and the probability linear discriminant analysis (PLDA) algorithms have been the state-of-the-art for text-independent speaker verification. This study combines a support vector machine (SVM) with the PLDA. The low dimensional i-vectors of the total variability system are used as the inputs to the support vector machine, with the cosine kernel function used to achieve better discrimination. This method achieves considerable performance improvement with the PLDA system. Furthermore, the score fusion of the SVM with the PLDA give even better results. Tests were conducted on the female part of the interview section of the NIST 2012 core test corpus. The detection cost function (DCF) of the fusion system was reduced by 25.1% for common condition 1 and 25.2% for condition 3 compared with the best results for a single system.
郭武, 张圣, 徐杰, 胡国平, 马啸空. 全变量系统和支持向量机结合的说话人确认[J]. 清华大学学报（自然科学版）, 2017, 57(3): 240-243.
GUO Wu, ZHANG Sheng, XU Jie, HU Guoping, MA Xiaokong. Speaker verification based on SVM and total variability. Journal of Tsinghua University(Science and Technology), 2017, 57(3): 240-243.
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