Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort

Kovac D, Mekyska J, Aharonson V, Harar P, Galaz Z, Rapcsak S, Orozco-Arroyave JR, Brabenec L, Rektorova I (2024)

Publication Type: Journal article

Publication year: 2024

Journal

Biomedical Signal Processing and Control Elsevier

Book Volume: 88

Article Number: 105667

DOI: 10.1016/j.bspc.2023.105667

Abstract

Hypokinetic dysarthria, a motor speech disorder characterized by reduced movement and control in the speech-related muscles, is mostly associated with Parkinson's disease. Acoustic speech features thus offer the potential for early digital biomarkers to diagnose and monitor the progression of this disease. However, the influence of language on the successful classification of healthy and dysarthric speech remains crucial. This paper explores the analysis of acoustic speech features, both established and newly proposed, in a multilingual context to support the diagnosis of PD. The study aims to identify language-independent and highly discriminative digital speech biomarkers using statistical analysis and machine learning techniques. The study analyzes thirty-three acoustic features extracted from Czech, American, Israeli, Columbian, and Italian PD patients, as well as healthy controls. The analysis employs correlation and statistical tests, descriptive statistics, and the XGBoost classifier. Feature importances and Shapley values are used to provide explanations for the classification results. The study reveals that the most discriminative features, with reduced language dependence, are those measuring the prominence of the second formant, monopitch, and the frequency of pauses during text reading. Classification accuracies range from 67% to 85%, depending on the language. This paper introduces the concept of language robustness as a desirable quality in digital speech biomarkers, ensuring consistent behaviour across languages. By leveraging this concept and employing additional metrics, the study proposes several language-independent digital speech biomarkers with high discrimination power for diagnosing PD.

Involved external institutions

Brno University of Technology (BUT) / Vysoké učení technické v Brně (VUT) CZ Czech Republic (CZ) The Academic College of Engineering in Tel Aviv (AFEKA) IL Israel (IL) University of Arizona US United States (USA) (US) Universidad de Antioquía (UDEA) CO Colombia (CO) Masaryk University CZ Czech Republic (CZ)

How to cite

APA:

Kovac, D., Mekyska, J., Aharonson, V., Harar, P., Galaz, Z., Rapcsak, S.,... Rektorova, I. (2024). Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort. Biomedical Signal Processing and Control, 88. https://dx.doi.org/10.1016/j.bspc.2023.105667

MLA:

Kovac, Daniel, et al. "Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort." Biomedical Signal Processing and Control 88 (2024).

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