Improved Speaker Independent Dysarthria Intelligibility Classification Using Deepspeech Posteriors

Abstract

Individuals with dysarthria are unable to control rapid movement of the velum leading to reduction in intelligibility, audibility, naturalness and efficiency of vocal communication. Automatic intelligibility assessment of dysarthric patients allows clinicians diagnose the impact of therapy and medication and also to plan future course of action. Earlier works have concentrated on building speaker dependent machine learning systems for intelligibility assessment, due to limited availability of data. However, a speaker independent assessment system is of greater use by clinicians. Motivated by this observation, we propose a speaker independent intelligibility assessment system which relies on a novel set of features obtained by processing the output of DeepSpeech, an end to end Speech-to-Text engine. All experiments have been performed on the Universal Access Speech database. An accuracy of 53.9% was obtained using Support Vector Machine based four-class classification system for the speaker independent scenario while the accuracy obtained for the speaker dependent scenario is 97.4%.