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Voice Activity Detection in Noisy Environment based on Statistical Nonlinear Dimension Reduction Techniques  

Han Hag-Yong (동명정보대학교 정보공학부)
Lee Kwang-Seok (진주산업대학교 전자공학과)
Go Si-Yong (경일대학교 전자정보공학부)
Hur Kang-In (동아대학교 전자공학과)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.9, no.5, 2005 , pp. 986-994 More about this Journal
Abstract
This Paper proposes the likelihood-based nonlinear dimension reduction method of the speech feature parameters in order to construct the voice activity detecter adaptable in noisy environment. The proposed method uses the nonlinear values of the Gaussian probability density function with the new parameters for the speec/nonspeech class. We adapted Likelihood Ratio Test to find speech part and compared its performance with that of Linear Discriminant Analysis technique. In experiments we found that the proposed method has the similar results to that of Gaussian Mixture Models.
Keywords
Voice Activity Detection; Dimension Reduction; Linear Discriminant Analysis;
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