Robust voice activity detection based on the entropy of noise-suppressed spectrum

Abstract

A novel noise robust voice activity detection approach is introduced. The novelty of the method that it uses noise suppressed spectrum of the input signal for spectral entropy calculation. As a result excellent end-pointing performance is observed based on predefined global entropy threshold and time constraints. The effect of frame dropping controlled by the proposed algorithm was investigated on the accuracy of automatic speech recognition. The experiments were performed on Hungarian publicly available noisy and normal telephony speech databases. The relative improvement due to dropping of non-speech frames was positive in all test configurations with a maximum of 29,5%. Besides, in average more than 50% of the frames were dropped.