Detection and Classification of Neurodegenerative Diseases by Automatic Speech Analysis

This study presents a novel non-invasive method for detecting and classifying neuro-degenerative diseases such as Parkinson’s (PD) and Alzheimer’s (AD) through automatic speech analysis and artificial intelligence. The analysis of the voice recordings was carried out using different parametric extraction methods based on the MFCC and prosodic coefficients (VOT, Jitter, Shimmer, HNR, …) followed by a classification step based on CNN and FC-DNN neural network. These methods made it possible to extract relevant speech parameters and use them for training and classification. The results obtained showed vocal disturbances in mild and preclinical stages of PD and AD such as articulation, prosody and rhythmic abilities. Developed machine learning algorithms were able to detect subjects with PD with $98 \%$ accuracy from rapid syllable repetitions and $96 \%$ accuracy for subjects with AD from voice parameters.