Comparing Speech to Fine and Gross Motor Skills in Parkinson's Disease

Abstract

Parkinson’s Disease (PD) is a neurodegenerative motor disorder resulting from damage to dopaminergic neurons. The goal of this study is to evaluate the correspondence between speech and non-speech motor impairments. To explore this, we extract features from the mPower dataset [B. M. Bot et al., Sci Data 3, Art. 160011 (2016)] containing mobile data from PD patients and healthy controls along with their performance on a vowel phonation, finger tapping, and walking task. We hypothesize that there is a shared motor system underlying each of these modalities and that disease progression will manifest in impairments to both speech and non-speech systems that rely on motor control. For acoustic features, we measure temporal consistency via F0-independent features (shimmer, jitter, and harmonics-to-noise ratio). For non-acoustic tasks, we adapt this set to measure spatial consistency and accuracy in finger tapping or walking. We perform clustering and multidimensional scaling (MDS) on our features to understand their correspondence across the modalities. Results will be reported with relevance to the relationship between PD, and its effects on articulatory and general motor processes.