A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector
A Semi-Continuous State-Transition Probability HMM-Based Voice Activity Detector
Blog Article
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 jolly rancher filled gummies 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 merrick backcountry wet cat food very favorably with the adaptive multirate VAD, option 2 (AMR2).