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Automatic Detection of Parkinson’s Disease: An Experimental Analysis of Common Speech Production Tasks Used for Diagnosis

  • Anna PompiliEmail author
  • Alberto Abad
  • Paolo Romano
  • Isabel P. Martins
  • Rita Cardoso
  • Helena Santos
  • Joana Carvalho
  • Isabel Guimarães
  • Joaquim J. Ferreira
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10415)

Abstract

Parkinson’s disease (PD) is the second most common neurodegenerative disorder of mid-to-late life after Alzheimer’s disease. During the progression of the disease, most individuals with PD report impairments in speech due to deficits in phonation, articulation, prosody, and fluency. In the literature, several studies perform the automatic classification of speech of people with PD considering various types of acoustic information extracted from different speech tasks. Nevertheless, it is unclear which tasks are more important for an automatic classification of the disease. In this work, we compare the discriminant capabilities of eight verbal tasks designed to capture the major symptoms affecting speech. To this end, we introduce a new database of Portuguese speakers consisting of 65 healthy control and 75 PD subjects. For each task, an automatic classifier is built using feature sets and modeling approaches in compliance with the current state of the art. Experimental results permit to identify reading aloud prosodic sentences and story-telling tasks as the most useful for the automatic detection of PD.

Keywords

Parkinson’s disease Phonation Articulation Prosody 

Notes

Acknowledgments

This work was supported by Portuguese national funds through – Fundação para a Ciência e a Tecnologia (FCT), under Grants SFRH/BD/97187/2013 and Projects with reference UID/CEC/50021/2013 and CMUP-ERI/TIC/0033/2014.

References

  1. 1.
    Movement disorder society task force on rating scales for Parkinson’s disease. The Unified Parkinson’s Disease Rating Scale (UPDRS): Status and recommendations (2003)Google Scholar
  2. 2.
    Goberman, A.M., Coelho, C.: Acoustic analysis of Parkinsonian speech I: speech characteristics and L-Dopa therapy. NeuroRehabilitation 17(3), 237–246 (2002)Google Scholar
  3. 3.
    Bocklet, T., Steidl, S., Nöth, E., Skodda, S.: Automatic evaluation of Parkinson’s speech-acoustic, prosodic and voice related cues. In: Interspeech, pp. 1149–1153 (2013)Google Scholar
  4. 4.
    Orozco-Arroyave, J.R., Hönig, F., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Skodda, S., Rusz, J., Nöth, E.: Voiced/unvoiced transitions in speech as a potential bio-marker to detect Parkinson’s disease. In: Interspeech, pp. 95–99 (2015)Google Scholar
  5. 5.
    Orozco-Arroyave, J.R., Belalcázar-Bolaños, E.A., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Haderlein, T., Nöth, E.: Phonation and articulation analysis of Spanish vowels for automatic detection of Parkinson’s disease. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2014. LNCS, vol. 8655, pp. 374–381. Springer, Cham (2014). doi: 10.1007/978-3-319-10816-2_45 Google Scholar
  6. 6.
    Bayestehtashk, A., Asgari, M., Shafran, I., McNames, J.: Fully automated assessment of the severity of Parkinson’s disease from speech. Comput. Speech Lang. 29(1), 172–185 (2015)CrossRefGoogle Scholar
  7. 7.
    Arias-Vergara, T., Vasquez-Correa, J., Orozco-Arroyave, J.R., Vargas-Bonilla, J.F., Nöth, E.: Parkinson’s disease progression assessment from speech using GMM-UBM. In: Interspeech, pp. 1933–1937 (2016)Google Scholar
  8. 8.
    Orozco-Arroyave, J.R., Vasquez-Correa, J., Hönig, F., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Skodda, S., Rusz, J., Noth, E.: Towards an automatic monitoring of the neurological state of Parkinson’s patients from speech. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6490–6494. IEEE (2016)Google Scholar
  9. 9.
    Eyben, F., Wöllmer, M., Schuller, B.: Opensmile: the Munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM International Conference on Multimedia, MM 2010, pp. 1459–1462. ACM, New York (2010)Google Scholar
  10. 10.
    Schuller, B., Steidl, S., Batliner, A., Burkhardt, F., Devillers, L., Müller, C., Narayanan, S.: The INTERSPEECH 2010 paralinguistic challenge. In: Interspeech (2010)Google Scholar
  11. 11.
    Proença, J., Veiga, A., Candeias, S., Perdigão, F.: Acoustic, phonetic and prosodic features of Parkinson’s disease speech. In: STIL-IX Brazilian Symposium in Information and Human Language Technology, 2nd Brazilian Conference on Intelligent Systems, Brazil (2013)Google Scholar
  12. 12.
    Pinto, S., Cardoso, R., Sadat, J., Guimarães, I., Mercier, C., Santos, H., Atkinson-Clement, C., Carvalho, J., Welby, P., Oliveira, P., D’Imperio, M., Frota, S., Letanneux, A., Vigario, M., Cruz, M., Martins, I.P., Viallet, F., Ferreira, J.J.: Dysarthria in individuals with Parkinson’s disease: a protocol for a binational, cross-sectional, case-controlled study in French and European Portuguese (FraLusoPark). BMJ Open 6(11), e12885 (2016)CrossRefGoogle Scholar
  13. 13.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  14. 14.
    Hanson, D.G., Gerratt, B.R., Ward, P.H.: Cinegraphic observations of laryngeal function in Parkinson’s disease. Laryngoscope 94(3), 348–353 (1984)CrossRefGoogle Scholar
  15. 15.
    Perez, K.S., Ramig, L.O., Smith, M.E., Dromey, C.: The Parkinson larynx: tremor and videostroboscopic findings. J. Voice 10(4), 354–361 (1996)CrossRefGoogle Scholar
  16. 16.
    Skodda, S., Visser, W., Schlegel, U.: Vowel articulation in Parkinson’s disease. J. Voice 25(4), 467–472 (2011)CrossRefGoogle Scholar
  17. 17.
    Rusz, J., Cmejla, R., Ruzickova, H., Ruzicka, E.: Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson’s disease. J. Acoust. Soc. Am. 129(1), 350–367 (2011)CrossRefGoogle Scholar
  18. 18.
    Vásquez-Correa, J., Orozco-Arroyave, J.R., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Nöth, E.: Design and implementation of an embedded system for real time analysis of speech from people with Parkinson’s disease. In: Symposium of Signals, Images and Artificial Vision - 2013, STSIVA - 2013, pp. 1–5, September 2013Google Scholar
  19. 19.
    Skodda, S., Schlegel, U.: Speech rate and rhythm in Parkinson’s disease. Mov. Disord. 23(7), 985–992 (2008)CrossRefGoogle Scholar
  20. 20.
    Eyben, F., Scherer, K.R., Schuller, B.W., Sundberg, J., Andr, E., Busso, C., Devillers, L.Y., Epps, J., Laukka, P., Narayanan, S.S., Truong, K.P.: The Geneva Minimalistic Acoustic Parameter Set (GeMAPS) for voice research and affective computing. IEEE Trans. Affect. Comput. 7(2), 190–202 (2016)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.INESC-ID/ISTLisbonPortugal
  2. 2.Laboratório de Estudos de Linguagem, Faculty of MedicineUniversity of LisbonLisbonPortugal
  3. 3.Department of Speech TherapyEscola Superior de Saúde do Alcoitão, SCMLEstorilPortugal
  4. 4.Instituto de Medicina MolecularLisbonPortugal
  5. 5.Laboratory of Clinical Pharmacology and Therapeutics, Faculty of MedicineUniversity of LisbonLisbonPortugal
  6. 6.CNS - Campus Neurológico SéniorTorres VedrasPortugal

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