Enhanced voice activity detection in kernel subspace domain
Dong Kook Kim, Jong Won Shin, Joon‐Hyuk Chang · The Journal of the Acoustical Society of America · 2013
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.