AI Article Synopsis
- - This paper introduces a new method for detecting voice activity using advanced statistical techniques in a complex feature space created through nonlinear mapping.
- - It utilizes a Gaussian density model along with kernel principal component analysis to capture the unique characteristics of speech signals.
- - The method features a decision-making process that relies on a multiple observation likelihood ratio test applied in the kernel space to improve detection accuracy.
Article 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.
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| http://dx.doi.org/10.1121/1.4747325 | DOI Listing |
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