Statistical Voice Activity Detection Using Low-Variance Spectrum Estimation and an Adaptive Threshold
ALAN H. DAVIS, Sven Erik Nordholm, Roberto B. Togneri · IEEE Transactions on Audio Speech and Language Processing · 2006
Traditionally, voice activity detection algorithms are based on any combination of general speech properties such as temporal energy variations, periodicity, and spectrum. This paper describes a novel statistical method for voice activity detection using a signal-to-noise ratio measure. The method employs a low-variance spectrum estimate and determines an optimal threshold based on the estimated noise statistics. A possible implementation is presented and evaluated over a large test set and compared to current modern standardized algorithms. The evaluations indicate promising results with the proposed scheme being comparable or favorable over the whole test set.