Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F23%3APU149323" target="_blank" >RIV/00216305:26220/23:PU149323 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1016/j.bspc.2023.105667" target="_blank" >https://doi.org/10.1016/j.bspc.2023.105667</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2023.105667" target="_blank" >10.1016/j.bspc.2023.105667</a>

Result language

angličtina

Original language name

Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

BIOMED SIGNAL PROCES

ISSN

1746-8094

e-ISSN

1746-8108

Volume of the periodical

88

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

11

Pages from-to

1-11

UT code for WoS article

001108281100001

EID of the result in the Scopus database

2-s2.0-85175611112