title: Statistical voice activity detection in kernel space.
abstract: This paper proposes a statistical voice activity detection method in a high-dimensional kernel feature space by a nonlinear mapping. A Gaussian density model is presented using kernel principal component analysis to represent the nonlinear characteristics of the speech signal. The proposed approach offers a decision rule based on a multiple observation likelihood ratio test in the kernel space.
authors.affiliation: School of Electronic and Computer Engineering, Chonnam National University, Yongbong, 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; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D009622:Noise; D017711:Nonlinear Dynamics; D025341:Principal Component Analysis; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013060:Speech; D013068:Speech Production Measurement / Q000379:methods*; D013997:Time Factors; D014831:Voice