New Cues in Low-Frequency of Speech for Automatic Detection of Parkinson’s Disease

  • E. A. Belalcazar-Bolaños
  • J. R. Orozco-Arroyave
  • J. F. Vargas-Bonilla
  • J. D. Arias-Londoño
  • C. G. Castellanos-Domínguez
  • Elmar Nöth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7930)

Abstract

In this paper, the analysis of low-frequency zone of the speech signals from the five Spanish vowels, by means of the Teager energy operator (TEO) and the modified group delay functions (MGDF) is proposed for the automatic detection of Parkinson’s disease.

According to our findings, different implementations of the TEO are suitable for tackling the problem of the automatic detection of Parkinson’s disease. Additionally, the application of MGDF for improving the resolution of the speech spectrum in the low frequency zone is able for enhancing differences exhibited between the first two formants from speech of people with Parkinson’s disease and healthy controls.

The best results are obtained for vowel /e/, where accuracy rates of up to 92% are achieved. This is an interesting result specially if it is considered that there are muscles that are involved in the production of the vowel /e/ but not in other vowels.

Keywords

Group Delay Functions Parkinson’s disease Teager Energy Operator 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ho, A., Iansek, R., Marigliani, C., Bradshaw, J., Gates, S.: Speech impairment in a large sample of patients with parkinson’s disease. Behavioral Neurology 11, 131–137 (1998)Google Scholar
  2. 2.
    Ramig, L., Fox, C., Shimon, S.: Speech treatment for parkinson’s disease. Expert Review Neurotherapeutics 8(2), 297–309 (2008)CrossRefGoogle Scholar
  3. 3.
    McNeil, M.: Clinical Management of Sensorimotor Speech Disorders, 2nd edn. Thieme, New York (2009)Google Scholar
  4. 4.
    Tjaden, K.: Speech and swallowing in parkinson’s disease. Top Geriatr Rehabilitation 24(2), 115–126 (2008)Google Scholar
  5. 5.
    Aronson, A., Bless, D.: Clinical voice disorders, 4th edn. Thieme, New York (2009)Google Scholar
  6. 6.
    Kent, R., Weismer, G., Kent, J., Vorperian, H., Duffy, J.: Acoustic studies of dysarthric speech: methods, progress, and potential. Journal of Communication Disorders 32(3), 141–180 (1999)CrossRefGoogle Scholar
  7. 7.
    Lee, G., Wang, C., Yang, C., Kuo, B.: Voice low tone to high tone ratio: A potential quantitative index for vowel [a:] and its nasalization 53(7), 1437–1439 (2006)Google Scholar
  8. 8.
    Vijayalakshmi, P., Reddy, M.: Assessment of dysarthric speech and an analysis on velopharyngeal incompetence. In: Proceedings of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 3759–3762 (2006)Google Scholar
  9. 9.
    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
  10. 10.
    Giovanni, A., Ouaknine, M., Guelfucci, R., Yu, T., Zanaret, M., Triglia, J.: Nonlinear behavior of vocal fold vibration: the role of coupling between the vocal folds. Journal of Voice 13(4), 456–476 (1999)CrossRefGoogle Scholar
  11. 11.
    Tsnas, A., Little, M., McSharry, P., Spielman, J., Ramig, L.: Novel speech signal processing algorithms for high-accuracy classification of parkinson’s disease 59(5), 1264–1271 (2012)Google Scholar
  12. 12.
    Kaiser, J.F.: On a simple algorithm to calculate the “energy” of a signal. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 1, pp. 381–384 (1990)Google Scholar
  13. 13.
    Cairns, D., Hansen, J., Riski, J.: A noninvasive technique for detecting hypernasal speech using a nonlinear operator. IEEE Transactions on Biomedical Engineering 43(1), 35 (1996)CrossRefGoogle Scholar
  14. 14.
    Ying, G., Mitchell, C., Jamieson, L.: Endpoint detection of isolated utterances based on a modified teager energy measurement. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, vol. 2, pp. 732–735 (1993)Google Scholar
  15. 15.
    Eivind, K.: Signal processing using the teager energy operator and other nonlinear operators (2003)Google Scholar
  16. 16.
    Yegnanaryana, B., Duncan, G., Murthy, H.: Formant extraction from group delay function. IEE Colloquium on Speech Processing, 2/1 –2/4 (1988)Google Scholar
  17. 17.
    Vijayalakshmi, P., Reddy, M., O’Shaughnessy, D.: Acoustic analysis and detection of hypernasality using a group delay function. Transactions on Biomedical Engineering 54(4), 621–629 (2007)CrossRefGoogle Scholar
  18. 18.
    Daza-Santacoloma, G., Arias-Londoño, J., Godino-Llorente, J., Sáenz-Lechón, N., Osma-Ruiz, V., Castellanos-Domínguez, C.G.: Dynamic feature extraction: an application to voice pathology detection. Intelligent Automation and Soft Computing 15(4), 665–680 (2009)Google Scholar
  19. 19.
    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
  20. 20.
    Phonetics, D.: Dissection of the speech production mechanism. Working Papers in Phonetics, UCLA (102), 1–89 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • E. A. Belalcazar-Bolaños
    • 1
  • J. R. Orozco-Arroyave
    • 1
    • 3
  • J. F. Vargas-Bonilla
    • 1
  • J. D. Arias-Londoño
    • 1
  • C. G. Castellanos-Domínguez
    • 2
  • Elmar Nöth
    • 3
  1. 1.Universidad de AntioquiaMedellínColombia
  2. 2.Universidad Nacional de ColombiaManizalesColombia
  3. 3.Universität Erlangen-NürnbergErlangenGermany

Personalised recommendations