Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold

Traditionally voice activity detection algorithms are based on any combination of general speech properties such as temporal energy variations, periodicity, and spectrum. This paper describes a novel statistical method for voice activity detection using a signal to noise ratio measure. The method e...

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Main Authors: Davis, A., Nordholm, Sven, Togneri, R.
Format: Journal Article
Published: IEEE Signal Processing Society 2006
Subjects:
Online Access:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1597247
http://hdl.handle.net/20.500.11937/37889
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author Davis, A.
Nordholm, Sven
Togneri, R.
author_facet Davis, A.
Nordholm, Sven
Togneri, R.
author_sort Davis, A.
building Curtin Institutional Repository
collection Online Access
description Traditionally voice activity detection algorithms are based on any combination of general speech properties such as temporal energy variations, periodicity, and spectrum. This paper describes a novel statistical method for voice activity detection using a signal to noise ratio measure. The method employs a low-variance spectrum estimate and determines an optimal threshold based on the estimated noise statistics. A possible implementation is presented and evaluated over a large test set and compared to current modern standardized algorithms. The evaluations indicate promising results with the proposed scheme being comparable or favorable over the whole test set.
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institution Curtin University Malaysia
institution_category Local University
last_indexed 2025-11-14T08:52:04Z
publishDate 2006
publisher IEEE Signal Processing Society
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spelling curtin-20.500.11937-378892017-01-30T14:09:29Z Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold Davis, A. Nordholm, Sven Togneri, R. voice activity detector - VAD Voice activity detection statistical decision adaptive voice activity detection Traditionally voice activity detection algorithms are based on any combination of general speech properties such as temporal energy variations, periodicity, and spectrum. This paper describes a novel statistical method for voice activity detection using a signal to noise ratio measure. The method employs a low-variance spectrum estimate and determines an optimal threshold based on the estimated noise statistics. A possible implementation is presented and evaluated over a large test set and compared to current modern standardized algorithms. The evaluations indicate promising results with the proposed scheme being comparable or favorable over the whole test set. 2006 Journal Article http://hdl.handle.net/20.500.11937/37889 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1597247 IEEE Signal Processing Society restricted
spellingShingle voice activity detector
- VAD
Voice activity detection
statistical decision
adaptive voice activity detection
Davis, A.
Nordholm, Sven
Togneri, R.
Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold
title Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold
title_full Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold
title_fullStr Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold
title_full_unstemmed Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold
title_short Statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold
title_sort statistical voice activity detection using low-variance spectrum estimation and an adaptive threshold
topic voice activity detector
- VAD
Voice activity detection
statistical decision
adaptive voice activity detection
url http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1597247
http://hdl.handle.net/20.500.11937/37889