Statistical voice activity detection in kernel space | Zendy

Dong Kook Kim | Zendy; JoonHyuk Chang | Zendy

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Statistical voice activity detection in kernel space

Author(s) -

Dong Kook Kim,

JoonHyuk Chang

Publication year - 2012

Publication title -

the journal of the acoustical society of america

Language(s) - Uncategorized

Resource type - Journals

SCImago Journal Rank - 0.619

H-Index - 187

eISSN - 1520-8524

pISSN - 0001-4966

DOI - 10.1121/1.4747325

Subject(s) - kernel principal component analysis , kernel (algebra) , pattern recognition (psychology) , variable kernel density estimation , kernel method , space (punctuation) , kernel density estimation , nonlinear system , computer science , mathematics , feature vector , gaussian , kernel embedding of distributions , artificial intelligence , principal component analysis , feature (linguistics) , statistics , support vector machine , physics , discrete mathematics , linguistics , philosophy , quantum mechanics , estimator , operating system

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.

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