Partially observable Markov decision process (POMDP) has been generally used to model agent decision processes such as dialogue management. In this paper, possibility of applying POMDP to a voice activity detector (VAD) has been explored. The proposed system first formulates hypotheses about the current noise environment and speech activity. Then, it decides and observes the features that are expected to be the most salient in the estimated situation. VAD decision is made based on the accumulated information. A comparative evaluation is presented to show that the proposed method outperforms other model-based algorithms regardless of noise types or signal-to-noise ratio.
Bibliographic reference. Park, Chiyoun / Kim, Namhoon / Cho, Jeongmi (2009): "Voice activity detection using partially observable Markov decision process", In INTERSPEECH-2009, 2227-2230.