Parkinson's Condition Estimation Using Speech Acoustic and Inversely Mapped Articulatory Data

Parkinson's disease is a neurological disorder that affects patient's motor function including speech articulation. There is no cure for Parkinson's disease. Speech and motor function declines as the disease progresses. Automatic assessment of the disease condition may advance the treatment of Parkinson's disease with objective, inexpensive measures. Speech acoustics, which can be easily obtained from patients, has been used for automatic assessment. The use of information in motor function of articulator (e.g., jaw, tongue, or lips) has rarely been investigated. In this paper, we proposed an approach of automatic assessment of Parkinson's condition using both acoustic data and acoustically-inverted articulatory data. The quasi-articulatory features were obtained from the Parkinson's acoustic speech data using acoustic-to-articulatory inverse mapping. Support vector regression (SVR) and deep neural network (DNN) regression were used in the experiment. Results indicated adding articulatory data to acoustic data can improve the performance of using acoustic data only, for both SVR and DNN. In addition, deep neural network outperformed support vector regression on the same data features measured with Pearson correlation but not with Spearman correlation. The implications of our approach with further improvement were discussed.