Zeynep Z. Sonkaya

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.

Increased interest in the relationships between the brain and behavior over the past several decades has made brain network process of emotional prosody a topic of study in disciplines like neurology, psychiatry, neurolinguistics and neuroscience. Because emotional prosody has a key role to implication of timbre component, mood sense, and prosodic content. Also, it serves a highly important function for sense, the meaning to be reflected, and ability to provide effective communication. Therefore, the knowledge of how the emotional prosody sequence works in the brain will contribute to both language development and foreign language teaching as well as clinical evaluation of individuals with verbal communication difficulty. In the literature, neuroimaging and neurophysiological studies about investigating emotional prosody have produced controversial results in specifying similarities versus differences mother tongue acquisition and foreign learning neural networks. For this reason, this review study takes an interdisciplinary perspective to identify the neural networks of emotional prosody in mother tongue and foreign language learning process with different imaging modalities.