Using variational bayes free energy for unsupervised voice activity detection

This paper addresses the problem of voice active detection (VAD) in noisy environments. We introduce variational Bayes approach to EM for classification to replace the heuristic state machines. The variational Bayes approach provides an explicit approximation of the evidence called free energy. Free energy is used to assess the reliability of the classification model, and can be periodically updated with a small number of samples. We apply this scheme to the detection of invalid classification caused in noise-only portions for more reliable VAD, avoiding some of the heuristics conventionally used in many VAD algorithms. An experimental evaluation is conducted on the CENSREC-1-C database for VAD evaluation, and the proposed method gives a significant improvement.