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- Open Access
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)
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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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
- Markov Chain
- Feature Extraction
- Additional Cost
- Detection Error