A semi-continuous state transition probability HMM-based voice activity detection

In this paper, 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 adaptive multirate VAD, phase 2 (AMR2).