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EURASIP Journal on Audio, Speech, and Music Processing
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A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector

EURASIP Journal on Audio, Speech, and Music Processing volume 2007, Article number: 043218 (2007) Cite this article

Abstract

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).

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Authors and Affiliations

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

    H Othman & T Aboulnasr

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  1. H Othman
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  2. T Aboulnasr
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Correspondence to H Othman.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Othman, H., Aboulnasr, T. A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector. J AUDIO SPEECH MUSIC PROC. 2007, 043218 (2007). https://doi.org/10.1155/2007/43218

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  • DOI: https://doi.org/10.1155/2007/43218

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