Parkinson’s Disease Recognition by Speech Acoustic Parameters Classification

Thanks to improvement of means of communication performance and intelligent systems, research works to detect speech disorders by analysing voice signals are very promising. This paper demonstrates that dysarthria in people with Parkinson’s disease (PWP) can be diagnosed using a classification of the characteristics of their voices. For this purpose, we have experimented two types of classifiers, namely Bernoulli and multinomial naive Bayes in order to select the most pertinent features parameters for diagnosing PWP. The prediction accuracy achieved by using multinomial naive Bayes (NB) classifier model reaching 95 % is very encouraging.

[1]  Jack J. Jiang,et al.  Phonatory impairment in Parkinson's disease: evidence from nonlinear dynamic analysis and perturbation analysis. , 2007, Journal of voice : official journal of the Voice Foundation.

[2]  Bayya Yegnanarayana,et al.  Extraction and representation of prosodic features for language and speaker recognition , 2008, Speech Commun..

[3]  Hinrich Schütze,et al.  Introduction to information retrieval , 2008 .

[4]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

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

[6]  Raymond D. Kent.  Hearing and Believing , 1996 .

[7]  Mireia Farrús,et al.  Jitter and shimmer measurements for speaker recognition , 2007, INTERSPEECH.

[8]  R. Arefi Shirvan,et al.  Voice analysis for detecting Parkinson's disease using genetic algorithm and KNN classification method , 2011, 2011 18th Iranian Conference of Biomedical Engineering (ICBME).

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

[10]  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.

[11]  T. Bayes.  An essay towards solving a problem in the doctrine of chances , 2003 .

[12]  S. Sumathi,et al.  Data Mining Tasks, Techniques, and Applications , 2006 .

[13]  Vangelis Metsis,et al.  Spam Filtering with Naive Bayes - Which Naive Bayes? , 2006, CEAS.

[14]  Pedro Gómez Vilda,et al.  Diagnosis of vocal and voice disorders by the speech signal , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[15]  Alan C. Bovik.  What You See Is What You Learn [DSP Education] , 2010, IEEE Signal Processing Magazine.