Improving the intelligibility of dysarthric speech towards enhancing the effectiveness of speech therapy

Dysarthria is a neuro-motor disorder in which the muscles used for speech production and articulation are severely affected. Dysarthric patients are characterized by slow or slurred speech that is difficult to understand. This work aims at enhancing the intelligibility of dysarthric speech towards developing an effective speech therapy tool. In this therapy tool, enhanced speech is used for providing auditory feedback with a delay to instill confidence in the patients, so that they can improve their speech intelligibility gradually through relearning. Feature level transformation techniques based on linear predictive coding (LPC) coefficient mapping and frequency warping of LPC poles are experimented in this work. Speech utterances from Nemours dataset with mild and moderate dysarthria are used to study the effectiveness of the proposed algorithms. The quality of the transformed speech is evaluated using subjective and objective measures. A significant improvement in the intelligibility of speech was observed. Our method henceforth could be used to enhance the effectiveness of speech therapy, by encouraging the dysarthric patients talk more, thus helping in their fast rehabilitation.