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Novel statistical voice activity detectors


Novel statistical voice activity detectors

Sangwan, Abhijeet (2006) Novel statistical voice activity detectors. Masters thesis, Concordia University.

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In this thesis, we propose a few practical statistical voice activity detectors (VADs) which combine the voice activity information in the short-term and long-term statistics of the speech signal. Unlike most VADs, which assume that the cues to activity lie within the frame alone, the proposed VAD schemes seek information for activity in the current as well as the neighboring frames. Particularly, we develop primary and contextual detectors to process the short-term and long-term information, respectively. We use the perceptual Ephraim-Malah (PEM) model to develop three primary detectors based on the Bayesian, Neyman-Pearson (NP) and competitive NP (CNP) approaches. Moreover, upon viewing voice activity detection as a composite hypothesis where the prior signal-to-noise ratio (SNR) forms the free parameter, we reveal that a correlation between the prior SNR and the hypothesis exists, i.e., a high prior SNR is more likely to be associated with 'speech hypothesis' than the 'pause hypothesis' and vice-versa, and unlike the Bayesian and NP approaches, the CNP approach alone exploits this correlation

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering
Item Type:Thesis (Masters)
Authors:Sangwan, Abhijeet
Pagination:xiv, 100 leaves : ill. ; 28 cm.
Institution:Concordia University
Degree Name:M.A. Sc.
Program:Electrical and Computer Engineering
Thesis Supervisor(s):Zhu, Wei-Ping and Ahmad, M. Omair
Identification Number:LE 3 C66E44M 2006 S265
ID Code:8844
Deposited By: Concordia University Library
Deposited On:18 Aug 2011 18:37
Last Modified:13 Jul 2020 20:05
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