International Journal of Speech Technology

, Volume 22, Issue 4, pp 1021–1029 | Cite as

Discriminating Parkinson diseased and healthy people using modified MFCC filter bank approach

  • Savitha S. UpadhyaEmail author
  • A. N. Cheeran
  • J. H. Nirmal


In this paper a modified Mel scaled filter bank-based approach to discriminate people suffering from Parkinson disease (PD) in their early stages from healthy people using speech samples is proposed. Parkinson’s disease not only affects the muscular activities of the human body but also affects the speech of the diseased. So, the speech features of Parkinson affected people tend to vary and hence differ from the speech features of healthy people. In this paper, the speech feature used for discriminating the two groups is the Mel frequency cepstral coefficients (MFCC) extracted from speech samples of both the PD and healthy people. The traditional way of computing the MFCC coefficients involves the design of the Mel filter bank. These filters are usually designed according to the auditory or acoustic system of human ear which follows the Mel scale. In this study, modification to this Mel scaled bank of filters is done by varying its bandwidth in the region of interest to compute the feature, MFCC and its performance is then compared with the conventionally designed MFCC filter bank for the said application. The performance is compared in terms of classification accuracy using radial basis network classifier. The results show an improvement of 6.3% in the classification accuracy obtained using the proposed method.


Parkinson disease MFCC Filter bank Bandwidth Modified Mel filter bank 



  1. Benba, A., Jilbab, A., & Hammouch, A. (2016). Discriminating between patients with Parkinson’s and neurological diseases using cepstral analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering,24(10), 1100–1108.CrossRefGoogle Scholar
  2. Benba, A., Jilbab, A., Hammouch, A., & Sandabad, S. (2015). Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson’s disease. In IEEE 1st international conference on electrical and information technologies ICEIT2015 (pp. 300–304).Google Scholar
  3. Braga, D., Madureira, A. M., Coelho, L., & Ajith, R. (2019). Automatic detection of Parkinson’s disease based on acoustic analysis of speech. Engineering Applications of Artificial Intelligence,77, 148–158.CrossRefGoogle Scholar
  4. Do, M. N. (2016) An automatic speaker recognition system, Audio Visual Communications Laboratory, Swiss Federal Institute of Technology, Lausanne, Switzerland. Retrieved May, 2016, from
  5. Godino-Llorente, J. I., Gomez-Vilda, P., & Blanco-Velasco, M. (2006). Dimensionality reduction of a pathological voice quality assessment system based on gaussian mixture models and short-term cepstral parameters. IEEE Transactions on Biomedical Engineering,53(10), 1943–1953.CrossRefGoogle Scholar
  6. Han, W., Chan, C. F., Choy, C. S., Pun, K. P. (2006). An efficient MFCC extraction method in speech recognition. In Proceedings of the IEEE international symposium on circuits and systems (ISCAS’2006) (pp. 145–148).Google Scholar
  7. Hornykiewicz, G. O. (1998). Biochemical aspects of Parkinson’s disease”. Neurology,51, S2–S9.CrossRefGoogle Scholar
  8. Kopparapu, S & Narayana, L (2010). Choice of Mel filter bank in computing MFCC of a resampled speech. In International conference on information science, signal processing and their applications (ISSPA).Google Scholar
  9. Molau, S., Pitz, M., Schliitel, R., Ney, H. (2001). Computing Mel-frequency cepstral coefficients on the power spectrum”. In Proceedings of the IEEE international conference on acoustics, speech, and signal processing (ICASSP’2001) (pp. 73–76).Google Scholar
  10. Okan Sakar, C., Serbes, G., Gunduz, A., Tunc, H. C., Nizam, H., Sakar, B. E., et al. (2019). A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing Journal,74, 255–263.CrossRefGoogle Scholar
  11. Orozco-Arroyave, J. R. et al. (2013). Perceptual analysis of speech signals from people with Parkinson’s disease. In IWINAC 2013, Part 1, LNCS 7930 (pp. 201–211). Berlin Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
  12. Oung, Q. W., Basah, S. N., Muthusamy, H., Vijean, V., Lee, H. (2017) Evaluation of short-term cepstral based features for detection of Parkinson’s Disease severity levels through speech signals. In MUCET 2017 IOP publishing IOP conference series: Materials science and engineering (p. 318).Google Scholar
  13. Rusz, J., & Cmejla, R. (2011). Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease. Journal of the Acoustical Society of America,129(1), 350–367.CrossRefGoogle Scholar
  14. Shahbakhi, M., Far, D. T., & Tahami, E. (2014). Speech analysis for diagnosis of Parkinson’s disease using genetic algorithm and support vector machine. Journal of Biomedical Science and Engineering,7, 147–156.CrossRefGoogle Scholar
  15. Singh, S., & Xu, W. (2019). Robust detection of Parkinson’s Disease using harvested smartphone voice data: A telemedicine approach. Telemedicine and e-Health. Scholar
  16. Skowronski, M. & Harris, J. (2002). Increased MFCC filter bandwidth for noise-robust phoneme recognition. In IEEE international conference on acoustics, speech, and signal processing ICASSP 2002, (pp. 801–804).Google Scholar
  17. Skowronski, M. & Harris, J. (2003). Improving the filter bank of classic speech feature extraction algorithm. In Proceedings of the 2003 international symposiumon circuits and systems ISCAS 2003, (pp. 281–284).Google Scholar
  18. Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., & Ramig, L. O. (2012). Novel speech signal processing algorithms for high accuracy classification of Parkinson’s disease. IEEE Transactions on Biomedical Engineering,59(5), 1264–1271.CrossRefGoogle Scholar
  19. Upadhya, S. S., Cheeran, A. N., & Nirmal, J. H. (2018a). Thomson multitaper MFCC and PLP voice features for early detection of Parkinson disease. Elsevier’s Biomedical Signal Processing and Control,46, 293–301.CrossRefGoogle Scholar
  20. Upadhya, S. S., Cheeran, A. N., & Nirmal, J. H. (2018b). Multitaper perceptual linear prediction features of voice samples to discriminate healthy persons from early stage Parkinson diseased persons. International Journal Speech Technology,21(3), 391–399.CrossRefGoogle Scholar
  21. Vignolo, L. D., Rufiner, H. L., Milone, D. H. (2009). Genetic optimization of cepstrum filterbank for phoneme classification. In Proceedings of the second international conference on bio-inspired systems and signal processing (BIOSIGNALS 2009), pp. 179-185.Google Scholar
  22. Vizza, P., Tradigo, G., Mirarchi, D., Bossio, R. B., Lombardo, N., Arabia, G., et al. (2019). Methodologies of speech analysis for neurodegenerative diseases evaluation. International Journal of Medical Informatics,122, 45–54.CrossRefGoogle Scholar
  23. Wrigley, S. N. (2015) Speech recognition by dynamic time warping, Speech and Hearing Research Group, University of Sheffield, Sheffield S1 4DP, United Kingdom. Retrieved March, 2015, from stu/com326/sym.html.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Electronics and Telecommunication Engineering DepartmentFr. C. Rodrigues Institute of TechnologyNavi MumbaiIndia
  2. 2.Electrical Engineering DepartmentVeermata Jijabai Technological InstituteMumbaiIndia
  3. 3.Electronics Engineering DepartmentK J Somaiya College of EngineeringMumbaiIndia

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