Traditional Voice activity detection (VAD) algorithms are applied in a linear transformed space without any constraint. As a result, the VAD algorithms are not robust to noise interference. Considering the special characteristics of speech, we proposed a new speech feature extraction method by giving constraints on the processing space as a reproducing kernel Hilbert space (RKHS). In the RKHS, we regarded the speech estimation as a functional approximation problem. Under this framework, we could incorporate the nonlinear mapping functions in the approximation implicitly via a kernel function. The approximation function could capture the nonlinear and high-order statistical regularities of the speech. Our VAD algorithm is designed on the basis of the power energy in this regularized RKHS. Compared with a baseline and G.729B VAD algorithms, experimental results showed the promising advantages of our proposed algorithm.
Bibliographic reference. Lu, Xugang / Unoki, Masashi / Isotani, Ryosuke / Kawai, Hisashi / Nakamura, Satoshi (2010): "Voice activity detection in a reguarized reproducing kernel hilbert space", In INTERSPEECH-2010, 3086-3089.