Publications – Details
Performance Analysis of Wavelet-based Voice Activity Detection
- Authors:
- Marco Jeub, Dorothea Kolossa, Ramon Fernandez Astudillo, and Reinhold Orglmeister
- Book Title:
- Proceedings of the International Conference on Acoustics, including the 35th German Annual Conference on Acoustics (DAGA)
- Venue:
- Rotterdam, The Netherlands
- Organization:
- DEGA
- Publisher:
- Deutsche Gesellsch. f. Akustik
- Date:
- Mar. 2009
- Pages:
- 407–410
- 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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