Dempster-Shafer theory for enhanced statistical model-based voice activity detection. (January 2018)

• Record Type: Journal Article
• Authors: Park, Tae-Jun; Chang, Joon-Hyuk
• Is Part Of: Computer speech & language. Volume 47(2018)
• Page Start: 47
• Page End: 58
• Publication Date: 2018-01
• Subjects: Dempster-Shafer theory -- Voice activity detection -- Likelihood ratio test; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2017.07.001
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: We develop the voice activity detection based on DS theory. Three statistical model-based VADs are used as the baseline systems. Probabilities from the three VADs are combined to DS theory. Proposed system works well over the existing methods. 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.