Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation

TL;DR

This study explores the use of acoustic features from poem recitation to identify hypokinetic dysarthria in Parkinson's disease patients, achieving promising classification sensitivity with multivariate analysis.

Contribution

It introduces a novel approach using poem recitation and specific speech features for better detection of hypokinetic dysarthria in PD patients.

Findings

Univariate analysis sensitivity up to 71.72%.

Multivariate analysis sensitivity up to 83.42%.

Poem recitation is effective for dysarthria assessment.

Abstract

Up to 90 % of patients with Parkinson's disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dysfluency and speech quality deterioration extracted from a specialized poem recitation task to discriminate dysarthric and healthy speech. For this purpose, 152 speakers (53 healthy speakers, 99 PD patients) were examined. Only mildly strong correlation between speech features and clinical status of the speakers was observed. In the case of univariate classification analysis, sensitivity of 62.63% (imprecise articulation), 61.62% (dysprosody), 71.72% (speech dysfluency) and 59.60% (speech quality deterioration) was achieved. Multivariate classification analysis improved the classification performance. Sensitivity of 83.42% using only two features describing imprecise articulation and speech quality deterioration in HD was achieved. We showed the promising potential of the selected speech features and especially the use of poem recitation task to quantify and identify HD in PD.