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http://dx.doi.org/10.7776/ASK.2011.30.6.330

Improvement in Supervector Linear Kernel SVM for Speaker Identification Using Feature Enhancement and Training Length Adjustment  

So, Byung-Min (서울시립대학교 컴퓨터과학부)
Kim, Kyung-Wha (대검찰청 음성분석실)
Kim, Min-Seok (LG 전자기술원)
Yang, Il-Ho (서울시립대학교 컴퓨터과학부)
Kim, Myung-Jae (서울시립대학교 컴퓨터과학부)
Yu, Ha-Jin (서울시립대학교 컴퓨터과학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.30, no.6, 2011 , pp. 330-336 More about this Journal
Abstract
In this paper, we propose a new method to improve the performance of supervector linear kernel SVM (Support Vector Machine) for speaker identification. This method is based on splitting one training datum into several pieces of utterances. We use four different databases for evaluating performance and use PCA (Principal Component Analysis), GKPCA (Greedy Kernel PCA) and KMDA (Kernel Multimodal Discriminant Analysis) for feature enhancement. As a result, the proposed method shows improved performance for speaker identification using supervector linear kernel SVM.
Keywords
Speaker identification; SVM; PCA; GKPCA; KMDA;
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