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

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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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Correspondence to H Othman.

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Keywords

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