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

Abstract 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.

[1]  C. Brayne,et al.  A systematic review of depression and mental illness preceding Parkinson's disease , 2006, Acta neurologica Scandinavica.

[2]  Ahmed Hammouch,et al.  Discriminating Between Patients With Parkinson’s and Neurological Diseases Using Cepstral Analysis , 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[3]  Ahmed Hammouch,et al.  Voice assessments for detecting patients with Parkinson’s diseases using PCA and NPCA , 2016, Int. J. Speech Technol..

[4]  Resul Das,et al.  A comparison of multiple classification methods for diagnosis of Parkinson disease , 2010, Expert Syst. Appl..

[5]  John P. Klein,et al.  Survival Analysis, Software , 2005 .

[6]  R. Wielgat,et al.  HFCC based recognition of bird species , 2007, Signal Processing Algorithms, Architectures, Arrangements, and Applications SPA 2007.

[7]  Ahmed Hammouch,et al.  Detecting multiple system atrophy, Parkinson and other neurological disorders using voice analysis , 2017, Int. J. Speech Technol..

[8]  Penny Jennett,et al.  The effect of information technology on the physician workforce and health care in isolated communities: The Canadian picture , 1999, Journal of telemedicine and telecare.

[9]  Babak Shahbaba,et al.  Nonlinear Models Using Dirichlet Process Mixtures , 2007, J. Mach. Learn. Res..

[10]  Mark D Skowronski,et al.  Exploiting independent filter bandwidth of human factor cepstral coefficients in automatic speech recognition. , 2004, The Journal of the Acoustical Society of America.

[11]  D. Huse,et al.  Burden of illness in Parkinson's disease , 2005, Movement disorders : official journal of the Movement Disorder Society.

[12]  Max A. Little,et al.  Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's Disease , 2008, IEEE Transactions on Biomedical Engineering.

[13]  J. Jankovic.  Parkinson’s disease: clinical features and diagnosis , 2008, Journal of Neurology, Neurosurgery, and Psychiatry.

[14]  Fikret S. Gürgen,et al.  Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings , 2013, IEEE Journal of Biomedical and Health Informatics.

[15]  Ahmed Hammouch,et al.  Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinson’s disease and healthy people , 2016, International Journal of Speech Technology.