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@article{2371788,
author = {Kováč, Daniel and Mekyska, Jiří and Aharonson, Vered and Harar, Pavol and Galaz, Zoltan and Rapcsak, Steven and OrozcoandArroyave, Juan Rafael and Brabenec, Luboš and Rektorová, Irena},
article_location = {OXFORD},
article_number = {B},
doi = {http://dx.doi.org/10.1016/j.bspc.2023.105667},
keywords = {Hypokinetic dysarthria; Parkinson's disease; Multilingual study; Acoustic speech features; Statistical analysis; Machine learning},
language = {eng},
issn = {1746-8094},
journal = {Biomedical Signal Processing and Control},
title = {Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort},
url = {https://www.sciencedirect.com/science/article/pii/S174680942301100X?via%3Dihub},
volume = {88},
year = {2024}
}
TY - JOUR
ID - 2371788
AU - Kováč, Daniel - Mekyska, Jiří - Aharonson, Vered - Harar, Pavol - Galaz, Zoltan - Rapcsak, Steven - Orozco-Arroyave, Juan Rafael - Brabenec, Luboš - Rektorová, Irena
PY - 2024
TI - Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort
JF - Biomedical Signal Processing and Control
VL - 88
IS - B
SP - 1-11
EP - 1-11
PB - ELSEVIER SCI LTD
SN - 17468094
KW - Hypokinetic dysarthria
KW - Parkinson's disease
KW - Multilingual study
KW - Acoustic speech features
KW - Statistical analysis
KW - Machine learning
UR - https://www.sciencedirect.com/science/article/pii/S174680942301100X?via%3Dihub
N2 - 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.
ER -
KOVÁČ, Daniel, Jiří MEKYSKA, Vered AHARONSON, Pavol HARAR, Zoltan GALAZ, Steven RAPCSAK, Juan Rafael OROZCO-ARROYAVE, Luboš BRABENEC and Irena REKTOROVÁ. Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort. \textit{Biomedical Signal Processing and Control}. OXFORD: ELSEVIER SCI LTD, 2024, vol.~88, B, p.~1-11. ISSN~1746-8094. Available from: https://dx.doi.org/10.1016/j.bspc.2023.105667.
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