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 such as phonetic quality, prosody, and voice quality. Then, we find the best feature set satisfying a new feature selection criterion that 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 an root mean square error of 8.1 to subjectively rated scores in the range of 0 to 100, which is a promising performance that the system can be successfully applied to help a speech therapist diagnosing the degree of speech disorder.

Index Terms: Dysarthria, feature selection, speech dimension, speech intelligibility, support vector regression