Automatic Assessment of Dysarthric Speech Intelligibility Based on Selected Phonetic Quality Features

Abstract

This paper addresses the problem of assessing the speech intelligibility of patients with dysarthria, which is a motor speech disorder. Dysarthric speech produces spectral distortion caused by poor articulation. To characterize the distorted spectral information, several features related to phonetic quality are extracted. Then, we find the best feature set which not only produces a small prediction error but also keeps their mutual dependency low. Finally, the selected features are linearly combined using a multiple regression model. Evaluation of the proposed method on a database of 94 patients with dysarthria proves the effectiveness in predicting subjectively rated scores.