Using Human Factor Cepstral Coefficient on Multiple Types of Voice Recordings for Detecting Patients with Parkinson's Disease

In this study, we wanted to discriminate between two groups of participants (patients with Parkinson's disease and healthy people) by analyzing 3 types of voice recordings. Firstly we collected multiple types of voice recording of three sustained vowels /a/, /o/ and /u/ at a comfortable level which was collected from the 40 participants (20 PD and 20 healthy). The technique used in this study is to extract Human Factor Cepstral Coefficients (HFCC). The extracted HFCC were compressed by calculating their average value in order to get the Voiceprint from each voice recording. Subsequently, a classification method was performed using Leave One Subject Out validation scheme along with supervised learning classifiers. We used SVM with its four different kernels (RBF, Linea, Polynomial and MLP), and k-nearest neighbored (k = 3, 5 and 7). Based on the research result, the best obtained classification accuracy was 87.5% using linear kernel of SVM with the first 14 cepstral coefficients of the HFCC and 100% using the test database. (C) 2017 AGBM. Published by Elsevier Masson SAS. All rights reserved.