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

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.