Automatic detection of expressed emotion in Parkinson's Disease

Patients with Parkinsons Disease (PD) frequently exhibit deficits in the production of emotional speech. In this paper, we examine the classification of emotional speech in patients with PD and the classification of PD speech. Participants were recorded speaking short statements with different emotional prosody which were classified with three methods (naïve Bayes, random forests, and support vector machines) using 209 unique auditory features. Feature sets were reduced using simple statistical testing. We achieve accuracies of 65.5% and 73.33% on classifying between the emotions and between PD vs. control, respectively. These results may assist in the future development of automated early detection systems for diagnosing patients with PD.