Statistics based features for unvoiced sound classification

Abstract

Unvoiced phonemes have significant presence in spoken English language. These phonemes are hard to classify, due to their weak energy and lack of periodicity. Sound textures such as sound made by a flowing stream of water or falling droplets of rain have similar aperiodic properties in temporal domain as unvoiced phonemes. These sounds are easily differentiated by a human ear. Recent studies on sound texture analysis and synthesis have shown that the human auditory system perceives sound textures using simple statistics. These statistics are obtained by decomposing sounds using a set of filter-banks and computing the moments of the filter responses, along with their correlation values. In this work we investigate if the above mentioned statistics, which are easy to extract, can also be used as features for classifying unvoiced sounds. To incorporate the moments and correlation values as features, a framework containing multiple classifiers is proposed. Experiments conducted on the TIMIT dataset gave an accuracy on par with the latest reported in the literature, with lesser computational cost.