Longitudinal Evaluation of Dysarthria Progression in Patients with Parkinson’s Disease

TL;DR

This study uses speech recordings to track how dysarthria worsens over time in Parkinson’s disease patients, identifying specific speech patterns that indicate disease progression.

Contribution

The study introduces a novel method using phonological posteriors and GRUs to model dysarthria progression in Parkinson’s disease.

Findings

Strident, dental, pause, back, and continuant phonological classes best explain dysarthria progression over two years.

Speech patterns can be used to automatically assess Parkinson’s disease progression and identify contributing phonological classes.

Abnormal speech production may reflect broader issues like respiration in Parkinson’s patients.

Abstract

Background/Objectives: Automatic evaluation of Parkinson’s disease (PD) progression is an emerging topic that deserves special attention from the research community. Unobtrusive, low-cost technology is essential for monitoring PD patients in remote areas. This paper proposes the use of phonological posteriors to create models that allow the progression of dysarthria level progression to be modelled based on speech recordings. Methods: Eighteen Gated Recurrent Units (GRUs) are used to estimate an equal number of phonological classes assigned to each phoneme pronounced in a given recording. Classification models of PD vs. healthy control (HC) subjects are trained with recordings of the PC-GITA corpus. This information is used in a separate corpus, with longitudinal recordings, to evaluate whether the progression of the dysarthria level, according to the modified Frenchay Dysarthria Assessment (mFDA), is related to abnormal production of specific phonemes. Results: Strident, dental, pause, back, and continuant phonological classes are the ones that better explain dysarthria level progression within time-frames of at least two years, therefore allowing possible monitoring of disease progression. Conclusions: Speech is a low-cost biosignal that can be used to automatically assess PD progression. In particular, this study shows that such an assessment makes it possible to evaluate dysarthria level progression and to find which phonological classes are contributing the most to such a progression. We believe that the findings reported in this paper provide objective evidence about possible abnormalities in broader speech-related processes like respiration, therefore contributing a better understanding of the relationship between speech production patterns and other speech-related processes affected when suffering from PD.

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