Integration of sporadic noise model in POMDP-based voice activity detection

Chiyoun Park, Namhoon Kim, Jeongmi Cho, Jeongsu Kim

Abstract: Partially observable Markov decision process (POMDP) has recently been applied to a voice activity detector (VAD), which makes it possible to incorporate knowledge about the recording environments in the decision process in order to achieve a more stable performance in noisy situations. In this paper, the model has been further explored to utilize prior knowledge about possible intermittent noise signals such as breath or click sounds, in addition to the stationary background noise types. The experimental result shows that application of sporadic noise models in a POMDP-based VAD reduces the equal error rate of the voice activity decision by about 7% relatively.