Assessing the dysarthria level of parkinson’s disease patients with gmm-ubm supervectors using phonological posteriors and diadochokinetic exercises

Abstract

Parkinson’s disease (PD) is a neuro-degenerative disorder that produces symptoms such as tremor, slowed movement, and a lack of coordination. One of the earliest indicators is a combination of different speech impairments called hypokinetic dysarthria. Some indicators that are prevalent in the speech of Parkinson’s patients include, imprecise production of stop consonants, vowel articulation impairment and reduced loudness. In this paper, we examine those features using phonological posterior probabilities obtained via parallel bidirectional recurrent neural networks. We also utilize information such as the velocity and acceleration curve of the signal envelope, and the peak amplitude slope and variance to model the quality of pronunciation for a given speaker. With our feature set, we train Gaussian Mixture Model based Universal Background Models for a set of training speakers and adapt a model for each individual speaker using a form of Bayesian adaptation. With the parameters describing each speaker model, we train SVM and Random Forest classifiers to discriminate PD patients and Healthy Controls (HC), and to determine the severity of dysarthria for each speaker compared with ratings assessed by expert phoneticians.