Voiceprint analysis using Perceptual Linear Prediction and Support Vector Machines for detecting persons with Parkinson’s disease

In the aim of developing 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 Perceptual Linear Prediction (PLP) 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 along with the Support Vector Machines (SVMs) with its different types of kernels, (i.e.; RBF, Linear and polynomial). The best classification accuracy achieved was 82.35% using the first 13 and 14 coefficients of the PLP by Linear kernels SVMs. Key-Words: Voice analysis, Parkinson’s disease, Voiceprint, Perceptual linear prediction, Support Vector Machines, Leave One Subject Out.