title: A subspace approach based on embedded prewhitening for voice activity detection.
abstract: This paper presents a subspace approach for voice activity detection (VAD). The proposed approach is based on an embedded prewhitening scheme for the simultaneous diagonalization of the clean speech and noise covariance matrices to provide a decision rule based on likelihood ratio test in signal subspace domain. Experimental results show that the proposed subspace-based VAD algorithm outperforms the method using a Gaussian model in a conventional discrete Fourier transform domain at the low signal-to-noise conditions.
authors.affiliation: School of Electronic and Computer Engineering, Chonnam National University, Yongbong, 500-757 Gwangju, Republic of Korea. dkim@chonnam.ac.kr
authors.forename: Dong Kook
authors.initials: DK
authors.lastname: Kim
authors.forename: Joon-Hyuk
authors.initials: JH
authors.lastname: Chang
mesh_terms: D000465:Algorithms; D003661:Decision Support Techniques; D005583:Fourier Analysis; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009622:Noise; D012815:Signal Processing, Computer-Assisted; D013060:Speech; D049250:Speech Recognition Software; D014831:Voice