A Soft Voice Activity Detection Using GARCH Filter and Variance Gamma Distribution

This paper presents a robust algorithm for a voice activity detector (VAD) based on generalized autoregressive conditional heteroscedasticity (GARCH) filter, variance gamma distribution (VGD), and adaptive threshold function. GARCH models are new statistical methods that are used especially in economic time series. There is a consensus that speech signals exhibit variances that change through time. GARCH models are a popular choice to model these changing variances. A speech signal is assumed to have a VGD because the VGD has heavier tails than the Gaussian distribution (GD). The distribution of noise signal is assumed to be Gaussian. In proposed method, heteroscedasticity will be modeled by GARCH, and then the parameters of the distributions will be estimated recursively. Finally, hard detection is the result of comparing a multiple observation likelihood ratio test (MOLRT) with an adaptive threshold function. The simulation results show that the proposed VAD is able to operate down to -5 dB and in nonstationary environments