An automatic diagnosis and assessment of dysarthric speech using speech disorder specific prosodic features

Abstract

To diagnose and classify the dysarthric speech, speech language pathologist (SLP) conducts a listening test. On the basis of the scores given by listeners the dysarthria is diagnosed and assessed. The above mentioned method is costly, time consuming and not very accurate. Unlike the traditional method, this research proposes an automatic diagnosis and assessment of dysarthria. The aim of this paper is to diagnose and classify the severity of dysarthria. The speech disorder specific prosodic features are selected by using genetic algorithm. The diagnosis and assessment of dysarthric speech is done by support vector machines. During diagnosis the classification accuracy of 98% has been achieved. And 87% of the dysarthric speech utterances are correctly classified. The standard UASPEECH database has been used in this work.