Automatic evaluation of parkinson's speech - acoustic, prosodic and voice related cues

Articulation and phonation is affected in 70% to 90% of patients with Parkinson’s disease (PD). This study focuses on the question whether speech carries information about 1. PD being present at a speaker or not, and 2. estimating the severity of PD (if present). We first perform classification experiments focusing on the automatic detection of PD as a 2-class problem (PD vs. healthy speakers). The detection of severity is described as a 3-class task based on the Unified Parkinson’s Disease Rating Scale (UPDRS) ratings. We employ acoustic, prosodic and glottal features on different kinds of speech tests: various syllable repetition tasks, read sentences and texts, and monologues. Classification is performed in either case by SVMs. We report recognition results of 81.9% when trying to differentiate between normally speaking persons and speakers with PD. With system fusion we achieved a recognition results of 59.1% on the task of UPDRS classification.