Perceptual Analysis of Speech Signals from People with Parkinson’s Disease

  • J. R. Orozco-Arroyave
  • J. D. Arias-Londoño
  • J. F. Vargas-Bonilla
  • Elmar Nöth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7930)

Abstract

Parkinson’s disease (PD) is a neurodegenerative disorder of the nervous central system and it affects the limbs motor control and the communication skills of the patients. The evolution of the disease can get to the point of affecting the intelligibility of the patient’s speech.

The treatments of the PD are mainly focused on improving limb symptoms and their impact on speech production is still unclear. Considering the impact of the PD in the intelligibility of the patients, this paper explores the discrimination capability of different perceptual features in the task of automatic classification of speech signals from people with Parkinson’s disease (PPD) and healthy controls (HC). The experiments presented in this paper are performed considering the five Spanish vowels uttered by 20 PPD and 20 HC.

The considered set of features includes linear prediction coefficients (LPC), linear prediction cepstral Coefficients (LPCC), Mel-frequency cepstral coefficients (MFCC), perceptual linear prediction coefficients (PLP) and two versions of the relative spectra coefficients (RASTA).

Accordin the results for vowels /e/ and /o/ it is not enough to consider one kind of perceptual features, it is required to perform combination of different coefficients such as PLP, MFCC and RASTA. For the case of the remaining vowels, the best results are obtained considering only one kind of perceptual features, PLP for vowel /a/ and MFCC for vowels /i/ and /u/.

Keywords

Perceptual analysis Parkinson’s disease linear prediction relative spectra analysis 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    de Rijk, M.: Prevalence of parkinson’s disease in europe: A collaborative study of population-based cohorts. Neurology 54, 21–23 (2000)Google Scholar
  2. 2.
    Sánchez, J., Buriticá, O., Pineda, D., Uribe, C., Palacio, L.: Prevalence of parkinson’s disease and parkinsonism in a colombian population using the capture-recapture method. International Journal of Neuroscience 113, 175–182 (2004)CrossRefGoogle Scholar
  3. 3.
    Skodda, S., Visser, W., Schlegel, U.: Vowel articulation in parkinson’s diease. Journal of Voice 25(4), 467–472 (2011)CrossRefGoogle Scholar
  4. 4.
    Hanson, D., Gerratt, B., Ward, P.: Cinegraphic observations of laryngeal function in parkinson’s disease. Laryngoscope 94(3), 348–353 (1984)CrossRefGoogle Scholar
  5. 5.
    Perez, K., Ramig, L., Smith, M., Dromery, C.: The parkinson larynx: tremor and videostroboscopic findings. Journal of Voice 10(4), 353–361 (1996)CrossRefGoogle Scholar
  6. 6.
    Weismer, G., Jeng, Y., Laures, J., Kent, R., Kent, J.: Acoustic and intelligibility characteristics of sentence production in neurogenic speech disorders. Folia Phoniatrica et Logopaedica 53, 1–18 (2001)CrossRefGoogle Scholar
  7. 7.
    Ramig, L., Fox, C., Shimon, S.: Speech treatment for parkinson’s disease. Expert Review Neurotherapeutics 8(2), 297–309 (2008)CrossRefGoogle Scholar
  8. 8.
    Bocklet, T., Nöth, E., Stemmer, G., Ruzickova, H., Rusz, J.: Detection of persons with parkinson’s disease by acoustic, vocal and prosodic analysis. In: Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 478–483 (2011)Google Scholar
  9. 9.
    Little, M.A., McSharry, P., Hunter, E., Spielman, J., Ramig, L.: Suitability of dysphonia measurements for telemonitoring of parkinson’s disease. IEEE Transactions on Bio-Medical Engineering 56(4), 1015–1022 (2009)CrossRefGoogle Scholar
  10. 10.
    Tsanas, A., Little, M., McSharry, P., Ramig, L.: Accurate telemonitoring of parkinson’s disease progression by noninvasive speech tests. IEEE Transactions on Biomedical Engineering 57(4), 884–893 (2010)CrossRefGoogle Scholar
  11. 11.
    Falk, T., Chan, W., Shein, F.: Characterization of atypical vocal source excitation, temporal dynamics and prosody for objective measurement of dysarthric word intelligibility. Speech Communication 54(5), 622–631 (2012)CrossRefGoogle Scholar
  12. 12.
    Buzo, A., Gray, A., Gray, R., Markel, J.: Speech coding based upon vector quantization. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 15–18 (1980)Google Scholar
  13. 13.
    Kim, H., Choi, S., Lee, H.: On approximating line spectral frequencies to lPC cepstral coefficients. IEEE Transactions on Speech and Audio Processing 8(2), 195–199 (2000)CrossRefGoogle Scholar
  14. 14.
    Godino-Llorente, J., Gómez-Vilda, P., Blanco-Velasco, M.: 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 (2006)CrossRefGoogle Scholar
  15. 15.
    Hermansky, H.: Perceptual linear predictive (plp) analysis of speech. Journal of the Acoustical Society of America 87(4), 1738–1752 (1990)CrossRefGoogle Scholar
  16. 16.
    Hermansky, H., Morgan, N.: Rasta processing of speech. IEEE Transactions on Speech and Audio Processing 2(4), 578–589 (1994)CrossRefGoogle Scholar
  17. 17.
    Orozco-Arroyave, J., Vargas-Bonilla, J., Arias-Londoño, J., Murillo-Rendón, S., Castellanos-Domínguez, G., Garcés, J.: Nonlinear dynamics for hypernasality detection in spanish vowels and words. Cognitive Computation 4(2), 1–10 (2012)Google Scholar
  18. 18.
    Rusz, J., Cmejla, R., Ruzickova, H., Ruzicka, E.: Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated parkinson’s disease. The Journal of the Acoustical Society of America 129(1), 350–367 (2011)CrossRefGoogle Scholar
  19. 19.
    Arias-Londoño, J., Godino-Llorente, J., Sáenz-Lechón, N., Osma-Ruiz, V., Castellanos-Domínguez, G.: An improved method for voice pathology detection by means of a hmm-based feature space transformation. Pattern Recognition 42, 3100–3112 (2010)CrossRefGoogle Scholar
  20. 20.
    Scholköpf, B., Smola, A.: Learning with Kernel. The MIT Press (2002)Google Scholar
  21. 21.
    Sáenz-Lechón, N., Godino-Llorente, J., Osma-Ruiz, V., Gómez-Vilda, P.: Methodological issues in the development of automatic systems for voice pathology detection. Biomedical Signal Processing and Control 1, 120–128 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • J. R. Orozco-Arroyave
    • 1
    • 2
  • J. D. Arias-Londoño
    • 1
  • J. F. Vargas-Bonilla
    • 1
  • Elmar Nöth
    • 2
  1. 1.Universidad de AntioquiaMedellínColombia
  2. 2.Friedrich Alexander UniversitätErlangen-NürnbergGermany

Personalised recommendations