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Performance Analysis of Wavelet-based Voice Activity Detection

Authors:
Jeub, M. ,  Kolossa, D. ,  Fernandez Astudillo, R. ,  Orglmeister, R.
Book Title:
Proceedings of the International Conference on Acoustics, including the 35th German Annual Conference on Acoustics (DAGA)
Organization:
DEGA
Publisher:
Deutsche Gesellsch. f. Akustik
Pages:
p.p. 407-410
Address:
Rotterdam, The Netherlands
Date:
Mar. 2009
ISBN:
978-3-98086-596-8
Language:
English

Abstract

The objective of this paper is to analyze the performance of wavelet-based voice activity detection algorithms (VAD) and to contrast it with that of the VAD standardized for the AMR-WB (Adaptive Multi-Rate Wideband) codec. Experimental results in clean, noisy and reverberant environments show that wavelet approaches lead to good results with respect to speech clipping and offer a much lower computational complexity. Integration of these algorithms into a Hidden Markov model (HMM) speech recognizer shows that the recognition performance using the AMR VAD can also be obtained or improved upon by wavelet based approaches, again at a notably reduced computational effort.

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