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Volume 14
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10.3390/brainsci14040384
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Correction published on 14 October 2024, see Brain Sci. 2024, 14(10), 1019.
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Article

Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis

by
Zeynep Z. Sonkaya
1,*,
Bilgin Özturk
2,
Rıza Sonkaya
2,
Esra Taskiran
3 and
Ömer Karadas
2
1
Department of Experimental Linguistics, Ankara University, 06590 Ankara, Turkey
2
Department of Neurology, Gülhane Medicine Faculty, Health Science University, 06010 Ankara, Turkey
3
Department of Neurology, Antalya Training and Research Hospital, 07100 Antalya, Turkey
*
Author to whom correspondence should be addressed.
Brain Sci. 2024, 14(4), 384; https://doi.org/10.3390/brainsci14040384
Submission received: 13 March 2024 / Revised: 11 April 2024 / Accepted: 12 April 2024 / Published: 16 April 2024 / Corrected: 14 October 2024
(This article belongs to the Special Issue Artificial Intelligence Methods for Assessing Speech, Language, and Communication Functioning)
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Abstract

Multiple sclerosis (MS) is one of the chronic and neurodegenerative diseases of the central nervous system (CNS). It generally affects motor, sensory, cerebellar, cognitive, and language functions. It is thought that identifying MS speech disorders using quantitative methods will make a significant contribution to physicians in the diagnosis and follow-up of MS patients. In this study, it was aimed to investigate the speech disorders of MS via objective speech analysis techniques. The study was conducted on 20 patients diagnosed with MS according to McDonald’s 2017 criteria and 20 healthy volunteers without any speech or voice pathology. Speech data obtained from patients and healthy individuals were analyzed with the PRAAT speech analysis program, and classification algorithms were tested to determine the most effective classifier in separating specific speech features of MS disease. As a result of the study, the K-nearest neighbor algorithm (K-NN) was found to be the most successful classifier (95%) in distinguishing pathological sounds which were seen in MS patients from those in healthy individuals. The findings obtained in our study can be considered as preliminary data to determine the voice characteristics of MS patients.
Keywords: MS; speech disorders; quantitative speech analysis MS; speech disorders; quantitative speech analysis

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MDPI and ACS Style

Sonkaya, Z.Z.; Özturk, B.; Sonkaya, R.; Taskiran, E.; Karadas, Ö. Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis. Brain Sci. 2024, 14, 384. https://doi.org/10.3390/brainsci14040384

AMA Style

Sonkaya ZZ, Özturk B, Sonkaya R, Taskiran E, Karadas Ö. Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis. Brain Sciences. 2024; 14(4):384. https://doi.org/10.3390/brainsci14040384

Chicago/Turabian Style

Sonkaya, Zeynep Z., Bilgin Özturk, Rıza Sonkaya, Esra Taskiran, and Ömer Karadas. 2024. "Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis" Brain Sciences 14, no. 4: 384. https://doi.org/10.3390/brainsci14040384

APA Style

Sonkaya, Z. Z., Özturk, B., Sonkaya, R., Taskiran, E., & Karadas, Ö. (2024). Using Objective Speech Analysis Techniques for the Clinical Diagnosis and Assessment of Speech Disorders in Patients with Multiple Sclerosis. Brain Sciences, 14(4), 384. https://doi.org/10.3390/brainsci14040384

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