Voice activity detection based on combination of weighted sub-band features using auto-correlation function

Publication Type:

Conference Paper

Source:

DiSS-LPSS Joint Workshop 2010 - 5th Workshop on Disfluency in Spontaneous Speech and 2nd International Symposium on Linguistic Patterns in Spontaneous Speech, Tokyo, Japan, p.85-88 (2010)

URL:

http://www.isca-speech.org/archive/diss_lpss_2010/papers/dl10_085.pdf

Keywords:

auto-correlation, DiSS, feature combination, sub-band weighting, voice activity detection, wavelet packet transform

Abstract:

This paper shows the voice activity detection (VAD) based on combination of weighted sub-band features using autocorrelation function. According to the fact that the noise corruption on each sub-band is different from each other, so the estimated signal to noise ratio (SNR) is employed to weight utility rate of each frequency sub-band. Furthermore, a strategy of sub-band features combination is used to integrate all of weighted sub-band auto-correlation function feature parameter and to develop the combined feature parameter. Experimental results demonstrate that the proposed VAD achieves better performance than existing standard VADs at any noise level.

Notes:

University of Tokyo; September 25-26, 2010