Low band continuous speech system for voice pathologies identification

Abstract

This paper describes the impact of the signal bandwidth reduction in the identification of voice pathologies. The implemented systems evaluate the identification of 3 classes divided by healthy subjects, subjects diagnosed with physiological larynx pathologies and subjects diagnosed with neuromuscular larynx pathologies. Continuous speech signals are down-sampled to 4 kHz and the extracted spectral parameters are applied to a GMM classifier. No significant change in accuracy occurs, being possible to conclude that the low frequencies contain sufficient information to allow the classification of pathologies. A second objective is to test the effects of suppressing the voice activity detection and the increasing the analysis window length. In both cases the accuracy increases. In conclusion, a pathological voice identification system based on signals sampled at 4 kHz, without voice activity detection and with an analysis window length of 40 ms is proposed, getting 81.8% accuracy. The proposed system has also the advantage of reduces the storage memory and the processing time.