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

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.