Combination of Multiple Speech Dimensions for Automatic Assessment of Dysarthric Speech Intelligibility

This paper focuses on the problem of automatically assessing the speech intelligibility of patients with dysarthria, which is a motor speech disorder. To effectively capture the characteristics of the speech disorder, various features are extracted in three speech dimensions: phonetic quality, prosody, and voice quality. Then, we find the best feature set satisfying a new feature selection criterion in which the selected features produce small prediction errors as well as low mutual dependency among them. Finally, the selected features are combined using support vector regression. Evaluation of the proposed method on a database of 94 speakers with dysarthria yielded a root mean square error of 8.1 to subjectively rated scores in the range of 0 to 100. This is a promising performance in which the system can be successfully applied to help a speech therapist in diagnosing the degree of speech disorder.