Dempster-Shafer theory for enhanced statistical model-based voice activity detection

Abstract

In this paper, we propose to combine the posterior probabilities of voice activity derived from different statistical model-based algorithms for enhanced voice activity detection. For this, the Dempster-Shafer (DS) theory of evidence is employed to represent and combine the different probabilities estimated by three different statistical model-based VAD algorithms including the Sohn’s likelihood ratio test (LRT)-based method, smoothed LRT-based method, and multiple observation LRT-based method. By considering a generalization of the Bayesian framework and permitting the characterization of uncertainty and ignorance through the DS theory, the probability of an ignorant state is eliminated through the orthogonal sum of several speech presence probabilities, which results in the performance improvement when detecting voice activity. According to objective test results, it is discovered the proposed DS theory-based VAD method offers significant improvements over the conventional approaches.