| Paper: | MLSP-P7.3 | ||
| Session: | Pattern Recognition and Classification II | ||
| Time: | Friday, May 21, 15:30 - 17:30 | ||
| Presentation: | Poster | ||
| Topic: | Machine Learning for Signal Processing: Signal detection, Pattern Recognition and Classification | ||
| Title: | A SEMI-CONTINUOUS STATE TRANSITION PROBABILITY HMM-BASED VOICE ACTIVITY DETECTION | ||
| Authors: | Hisham Othman; University of Ottawa | ||
| Tyseer Aboulnasr; University of Ottawa | |||
| Abstract: | 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 softly are 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). | ||
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