Voice activity detection based on multiple statistical models

Title
Voice activity detection based on multiple statistical models
Authors
Chang, J.H.; Kim, N.S.; Mitra, S.K.
Keywords
discrete cosine transform (DCT), generalized gamma function, maximum likelihood
Issue Date
2006-06
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Abstract
One of the key issues in practical speech processing is to achieve robust voice activity detection (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ the Gaussian assumption in the discrete Fourier transform (DFT) domain, which, however, deviates from the real observation. In this paper, we propose a class of VAD algorithms based on several statistical models. In addition to the Gaussian model, we also incorporate the complex Laplacian and Gamma probability density functions to our analysis of statistical proper-, ties. With a goodness-of-fit tests, we analyze the statistical properties of the DFT spectra of the noisy speech under various noise conditions. Based on the statistical analysis, the likelihood ratio test under the given statistical models is established for the purpose of VAD. Since the statistical characteristics of the speech signal are differently affected by the noise types and levels, to cope with the time-varying environments, our approach is aimed at finding adaptively an appropriate statistical model in an online fashion. The performance of the proposed VAD approaches in both the stationary and nonstationary noise environments is evaluated with the aid-of an objective measure.
URI
http://dspace.inha.ac.kr/handle/10505/1826
ISSN
1053-587X
Appears in Collections:
College of Information Technology & Engineering (IT공과대학) > Electronic Engineering (전자공학) > Journal Papers, Reports(전자공학 논문, 보고서)
Files in This Item:
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