A Gaussian/Laplacian hybrid statistical voice activity detector for line spectral frequency-based speech coders

TitleA Gaussian/Laplacian hybrid statistical voice activity detector for line spectral frequency-based speech coders
Publication TypeConference Paper
Year of Publication2003
AuthorsOthman, H., and T. Aboulnasr
Conference NameMicro-NanoMechatronics and Human Science, 2003 IEEE International Symposium on
Pagination693 - 696 Vol. 2
Date Publisheddec.
Keywordsfalse detection rate, feature extraction, Gaussian distribution, Gaussian/Laplacian hybrid, hidden Markov model, hidden Markov models, ITU-T G.729, probability density, signal detection, statistical analysis, statistical voice activity detector, vocoders, voice activity detection algorithm
Abstract

In this paper we introduce a voice activity detection (VAD) algorithm that is based on a two-state hidden Markov model. The observation layer of the proposed model, that contains the state conditional probability density functions, is a Gaussian-Laplacian hybrid. The proposed algorithm provides a false detection rate that is significantly lower than that of G. 729 Annex B VAD. Given that it works in the domain of ITU-T G.729 parameters, it requires a minimal additional cost for feature extraction.

URLhttp://dx.doi.org/10.1109/MWSCAS.2003.1562381
DOI10.1109/MWSCAS.2003.1562381

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