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Statistical Voice Activity Defector Based on Signal Subspace Model  

Ryu, Kwang-Chun (전남대학교 전자컴퓨터공학과)
Kim, Dong-Kook (전남대학교 전자컴퓨터공학과)
Publication Information
The Journal of the Acoustical Society of Korea / v.27, no.7, 2008 , pp. 372-378 More about this Journal
Abstract
Voice activity detectors (VAD) are important in wireless communication and speech signal processing, In the conventional VAD methods, an expression for the likelihood ratio test (LRT) based on statistical models is derived in discrete Fourier transform (DFT) domain, Then, speech or noise is decided by comparing the value of the expression with a threshold, This paper presents a new statistical VAD method based on a signal subspace approach, The probabilistic principal component analysis (PPCA) is employed to obtain a signal subspace model that incorporates probabilistic model of noisy signal to the signal subspace method, The proposed approach provides a novel decision rule based on LRT in the signal subspace domain, Experimental results show that the proposed signal subspace model based VAD method outperforms those based on the widely used Gaussian distribution in DFT domain.
Keywords
Voice Activity Detection; Gaussian Distribution; Likelihood Ratio Test;
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Times Cited By KSCI : 2  (Citation Analysis)
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