Speech/non-speech discrimination based on contextual information integrated bispectrum LRT

This letter shows an effective statistical voice activity detection algorithm based on the integrated bispectrum, which is defined as a cross spectrum between the signal and its square and inherits the ability of higher order statistics to detect signals in noise with many other additional advantages: 1) its computation as a cross spectrum leads to significant computational savings, and 2) the variance of the estimator is of the same order as that of the power spectrum estimator. The decision rule is formulated in terms of an average likelihood ratio test (LRT) involving successive integrated bispectrum speech features. With these and other innovations, the proposed method reports significant improvements in speech/pause discrimination as well as in speech recognition over standardized techniques such as ITU-T G.729, ETSI AMR, and AFE VADs, and over recently published VADs.