Detection of Amyotrophic Lateral Sclerosis Based on Acoustic Voice Analysis Using the openSMILE Library
https://doi.org/10.35596/1729-7648-2025-23-6-96-102
Abstract
This paper examines the problem of automatically detecting signs of amyotrophic lateral sclerosis based on the analysis of the acoustic characteristics of a voice signal. The openSMILE library with the ComParE_2016 configuration was used to extract functional acoustic features of the voice. Audio recordings from the Minsk2020_ALS voice database, which includes recordings of both healthy patients and patients with amyotrophic lateral sclerosis, were used as input. Voice features were compared between groups using the nonparametric Mann–Whitney test and FDR correction for multiple comparisons, with separate analysis by gender. An experiment on classifying voice signals was conducted using a nested cross-validation procedure. The resulting classifiers had a correct detection probability of 75.0 % (for male voices) and 74.2 % (for female voices). Statistically significant acoustic parameters that may be useful in automated diagnostics and monitoring of amyotrophic lateral sclerosis were identified.
About the Authors
A. V. Mikhnevich
Belarusian State University of informatics and Radioelectronics
Belarus
Belarus
Postgraduate at the Department of Electronic Computing Facilities
220013, Minsk, P. Brovki St., 6
Tel.: +375 17 293-84-78
M. I. Vashkevich
Belarusian State University of informatics and Radioelectronics
Belarus
Belarus
Vashkevich Maxim Iosifovich, Dr. Sci. (Tech.), Professor at the Department of Electronic Computing Facilities
220013, Minsk, P. Brovki St., 6
Tel.: +375 17 293-84-78
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Review
For citations:
Mikhnevich A.V., Vashkevich M.I. Detection of Amyotrophic Lateral Sclerosis Based on Acoustic Voice Analysis Using the openSMILE Library. Doklady BGUIR. 2025;23(6):96-102. (In Russ.) https://doi.org/10.35596/1729-7648-2025-23-6-96-102
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