Automatic detection of Parkinson’s disease based on acoustic analysis of speech

Abstract

This paper proposes a methodology to detect early signs of Parkinson’s disease (PD) through free-speech in uncontrolled background conditions. The early detection mechanism uses signal and speech processing techniques integrated with machine learning algorithms. Three distinct speech databases containing patients’ recordings at different stages of the PD are used for estimation of the parameters during the training and evaluation stages. The results reveal the potential in using Random Forest (RF) or Support Vector Machine (SVM) techniques. Once tuned, these algorithms provide a reliable computational method for estimating the presence of PD with a very high accuracy.