Parkinsons disease Diagnosis using Mel frequency Cepstral Coefficients and Vector Quantization

Abstract

This paper investigates the adaptation of MFCCs to the diagnosis of Parkinson’s disease (PD). The aim of this study is to provide a novel method, suitable for keeping track of the evolution of the patient’s pathology: easy-to-use, fast, non-invasive for the patient, and affordable for the clinicians. This method will be complementary to the existing ones- the perceptual judgment and the usual objective measurement (jitter, airflows...) which remain time and human resource consuming. The system designed for this particular task relies on the Mel-Frequency Cepstral coefficients (MFCC) for feature extraction and Vector Quantization (VQ) for feature analysis which is the state-of-theart for speaker recognition.