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  • Research Article
  • Open Access

A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector

EURASIP Journal on Audio, Speech, and Music Processing20072007:043218

  • Received: 15 December 2005
  • Accepted: 28 November 2006
  • Published:


We introduce an efficient hidden Markov model-based voice activity detection (VAD) algorithm with time-variant state-transition probabilities in the underlying Markov chain. The transition probabilities vary in an exponential charge/discharge scheme and are softly merged with state conditional likelihood into a final VAD decision. Working in the domain of ITU-T G.729 parameters, with no additional cost for feature extraction, the proposed algorithm significantly outperforms G.729 Annex B VAD while providing a balanced tradeoff between clipping and false detection errors. The performance compares very favorably with the adaptive multirate VAD, option 2 (AMR2).


  • Markov Chain
  • Feature Extraction
  • Acoustics
  • Additional Cost
  • Detection Error


Authors’ Affiliations

School of Information Technology and Engineering, Faculty of Engineering, University of Ottawa, Ontario, K1N 6N5, Canada


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© H. Othman and T. Aboulnasr. 2007

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.