Effective Speech/Pause Discrimination Using an Integrated Bispectrum Likelihood Ratio Test

This paper shows an effective voice activity detector based on a statistical likelihood ratio test defined on the integrated bispectrum of the signal. It inherits the ability of higher order statistics to detect signals in noise with many other additional advantages: i) its computation as a cross spectrum leads to significant computational savings, and ii) the variance of the estimator is of the same order as that of the power spectrum estimator. The proposed method incorporates contextual information to the decision rule, a strategy that has reported significant improvements in speech detection accuracy and robust speech recognition applications. The experimental analysis conducted on the well-known AURORA databases has reported significant improvements over standardized techniques such as ITU G.729, AMRI, AMR2 and ESTI AFE VADs, as well as over recently published VADs