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

Authors:
A. Davis;S. Nordholm;R. Togneri
Affiliations:
Western Australian Telecommun. Res. Inst., Crawley, WA, Australia;-;-
Venue:
IEEE Transactions on Audio, Speech, and Language Processing
Year:
2006

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Abstract

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.