Using online model comparison in the Variational Bayes framework for online unsupervised Voice Activity Detection

This paper presents the use of online Variational Bayes method for online Voice Activity Detection (VAD) in an unsupervised context. In conventional VAD, the final step often relies on state machines whose parameters are heuristically tuned. The goal of this study is to propose a solid statistical scheme for VAD using online model comparison which is provided from the Variational Bayes framework. In this scheme, two models are estimated online in parallel: one for the noise-only situation, and the other for the noise-plus-signal situation The VAD decision is done automatically depending on the selected model. An experimental evaluation on the CENSREC-1-C database shows a significant improvement by the proposed method compared to conventional statistical VAD methods.