Early Detection of Parkinson’s and Alzheimer’s Diseases using the VOT_Mean Feature

Abstract

Alzheimer’s (AD) and Parkinson’s diseases (PD) are tw of the most common neurological diseases in the world. Several studies have been conducted on the identification of these diseases using speech and laryngeal disorders. Those symptoms can appear even at the early stages of AD and PD, but not in very specific and prominent ways. Voice Onset Time (VOT) is an acoustic specification of the stopping consonant that is commonly discussed in studies of phonetic perception. In this study, the VOT_Mean feature was explored to identify AD and PD early using /pa/, /ka/, and /ta/ syllables for the diadochokinetic task (DDK). VOT_Mean was calculated as the average of the first and the second VOT values (VOT_1 and VOT_2), corresponding to the second and the penultimate VOT measurement cycles. Experimental tests were performed on Tunisian Arabic and Spanish databases for the early detection of AD and PD respectively. The results showed a very high significance of VOT_Mean on the early detection of AD and PD. Moreover, the best results were achieved using the XGBoost (XGBT) algorithm as a classifier on the VOT_Mean feature.