In this paper, we wanted to discriminate between two groups of patients (patients who suffer from Parkinson's disease and patients who suffer from other neurological disorders). We collected a variety of voice samples from 50 subjects using different recording devices in different conditions. Subsequently, we analyzed and extracted features from these samples using three different Cepstral techniques; Mel frequency cepstral coefficients (MFCC), perceptual linear prediction (PLP), and ReAlitive SpecTrAl PLP (RASTA-PLP). For classification we used leave one subject out validation scheme along with five different supervised learning classifiers. The best obtained result was 90% using the first 11 coefficients of the PLP and linear SVM kernels.

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.Index Terms-Voice analysis, Parkinson's disease, MFCC, Voiceprint, LOSOVS, SVM.

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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.