Detecting Patients with Parkinson ’ s disease using Mel Frequency Cepstral Coefficients and Support Vector Machines

In order to develop the assessment of speech disorders for detecting patients with Parkinson’s disease (PD), we have collected 34 sustained vowel / a /, from 34 subjects including 17 PD patients. We subsequently extracted from 1 to 20 coefficients of the Mel Frequency Cepstral Coefficients (MFCCs) from each individual. To extract the voiceprint from each individual, we compressed the frames by calculating their average value. For classification, we used the Leave-One-Subject-Out (LOSO) validation scheme and the Support Vector Machines (SVMs) with its different types of kernels, (i.e.; RBF, Linear and polynomial). The best classification accuracy achieved was 91.18% using the first 12 coefficients of the MFCCs by Linear kernels SVMs.