Publication detail

Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort

KOVÁČ, D. MEKYSKA, J. AHARONSON, V. HARÁR, P. GALÁŽ, Z. RAPCSAK, S. OROZCO-ARROYAVE, J. R. BRABENEC, L. REKTOROVÁ, I.

Original Title

Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort

Type

journal article in Web of Science

Language

English

Original 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.

Keywords

Hypokinetic dysarthria; Parkinson’s disease; Multilingual study; Acoustic speech features; Statistical analysis; Machine learning

Authors

KOVÁČ, D.; MEKYSKA, J.; AHARONSON, V.; HARÁR, P.; GALÁŽ, Z.; RAPCSAK, S.; OROZCO-ARROYAVE, J. R.; BRABENEC, L.; REKTOROVÁ, I.

Released

4. 11. 2023

ISBN

1746-8094

Periodical

BIOMED SIGNAL PROCES

Year of study

88

Number

2

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

11

Pages count

11

URL

https://doi.org/10.1016/j.bspc.2023.105667

BibTex

@article{BUT185091,
  author="KOVÁČ, D. and MEKYSKA, J. and AHARONSON, V. and HARÁR, P. and GALÁŽ, Z. and RAPCSAK, S. and OROZCO-ARROYAVE, J. R. and BRABENEC, L. and REKTOROVÁ, I.",
  title="Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort",
  journal="BIOMED SIGNAL PROCES",
  year="2023",
  volume="88",
  number="2",
  pages="1--11",
  doi="10.1016/j.bspc.2023.105667",
  issn="1746-8094",
  url="https://doi.org/10.1016/j.bspc.2023.105667"
}