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

A. Cherif,Κ. Dabbabi,A. Kehili

doi:10.48084/etasr.4038

A. Cherif, Κ. Dabbabi + Show 1 more

Open Access

https://doi.org/10.48084/etasr.4038

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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.

Highlights

Associated speech disturbances caused by disturbances in the speech mechanism, in muscle control, are grouped under the single definition called dysarthria
The XGBT algorithm had 92% precision, 92% accuracy, 92% sensitivity, 92% F1-measure, and 0.84 Matthews Correlation Coefficient (MCC)
This study aimed to examine whether subtle early symptoms of Alzheimer’s disease (AD) and Parkinson's disease (PD) exist in the acoustic signal of Arabic and Spanish speech respectively, by comparing the VOT_Mean feature between HC, AD, and PD groups

Summary

Introduction

Associated speech disturbances caused by disturbances in the speech mechanism, in muscle control, are grouped under the single definition called dysarthria. A total of 212 patients having symptoms of joint dysarthria with different neurological disorders, such as Parkinsonism, amyotrophic lateral sclerosis, chorea and cerebellar ataxia, bulbar palsy, dystonia, and pseudobulbar palsy were examined, identifying 38 distinct characteristics of speech and categorizing them into seven modalities: articulation, respiration, pitch, prosody, loudness, resonance, and vocal quality. These studies specified the characteristics of each neurogenic group in addition to those shared by more than one. It is important to detect early Parkinson’s Disease Dementia (PDD)

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