title: Enhanced voice activity detection in kernel subspace domain.
abstract: This paper proposes a voice activity detection (VAD) method in a kernel subspace domain to improve the performance of the kernel-based VAD. A linear transform matrix that can simultaneously diagonalize the two covariance matrices using kernel principal component analysis is presented to generate the kernel subspace. The likelihood ratio test based on Gaussian distributions is applied for the VAD in the kernel subspace. Experimental results show that the proposed VAD algorithm outperforms the conventional approaches under various noise conditions.
authors.affiliation: School of Electronic and Computer Engineering, Chonnam National University, Gwangju 500-757, Korea. dkim@chonnam.ac.kr
authors.forename: Dong Kook
authors.initials: DK
authors.lastname: Kim
authors.forename: Jong Won
authors.initials: JW
authors.lastname: Shin
authors.forename: Joon-Hyuk
authors.initials: JH
authors.lastname: Chang
mesh_terms: D000465:Algorithms; D005583:Fourier Analysis; D006801:Humans; D016014:Linear Models; D016011:Normal Distribution; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013061:Speech Acoustics; D013067:Speech Perception; D014831:Voice