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Unsupervised Morphological Segmentation for Detecting Parkinson's Disease

The growth of life expectancy entails a rise in prevalence of aging-related neurodegenerative disorders, such as Parkinson’s disease. In the ongoing quest to find sensitive behavioral markers of this condition, computerized tools prove particularly promising. Here, we propose a novel method utilizing unsupervised morphological segmentation for accessing morphological properties of a speaker’s language. According to our experiments on German, our method can classify patients vs. healthy controls with 81 percent accuracy, and estimate the neurological state of PD patients with Pearson correlation of 0.46 with respect to the unified Parkinson’s disease rating scale. Our work is the first study to show that unsupervised morphological segmentation can be used for automatic detection of a neurological disorder.