Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson's disease

Parkinson's disease (PD) is a neurodegenerative disorder of unknown etiology. PD patients suffer from hypokinetic dysarthria, which manifests on all aspects of voice production, respiration, phonation, articulation, nasality and prosody. To evaluate these disorders, clinicians have adopted perceptual methods, based on acoustic cues, to distinguish the different disease states. To develop the assessment of voice disorders for detecting patients with Parkinson's disease (PD), we have used a PD dataset of 34 sustained vowel/a/, from 34 people including 17 PD patients. We then extracted from 1 to 20 coefficients of the Mel Frequency Cepstral Coefficients from each person. To extract the voiceprint from each voice sample, we compressed the frames by calculating their average value. For classification, we used Leave-One-Subject-Out validation-scheme along with the Support Vector Machines with its different types of kernels. The best classification accuracy achieved was 91.17% using the first 12 coefficients of the MFCC by Linear kernels SVM.