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Language Independent Assessment of Motor Impairments of Patients with Parkinson’s Disease Using i-Vectors

  • N. GarciaEmail author
  • J. C. Vásquez-Correa
  • J. R. Orozco-Arroyave
  • N. Dehak
  • E. Nöth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10415)

Abstract

Speech disorders are among the most common symptoms in patients with Parkinson’s disease. In recent years, several studies have aimed to analyze speech signals to detect and to monitor the progression of the disease. Most studies have analyzed speakers of a single language, even in that scenario the problem remains open. In this study, a cross-language experiment is performed to evaluate the motor impairments of the patients in three different languages: Czech, German and Spanish. The i-vector approach is used for the evaluation due to its capability to model speaker traits. The cosine distance between the i-vector of a test speaker and a reference i-vector that represents either healthy controls or patients is computed. This distance is used to perform two analyses: classification between patients and healthy speakers, and the prediction of the neurological state of the patients according to the MDS-UPDRS score. Classification accuracies of up to \(72\%\) and Spearman’s correlations of up to 0.41 are obtained between the cosine distance and the MDS-UPDRS score. This study is a step towards a language independent assessment of patients with neuro-degenerative disorders.

Keywords

Parkinson’s disease i-vectors UPDRS score Language independent assessment 

Notes

Acknowledgments

Thanks to CODI from University of Antioquia by the grant Numbers 2015-7683 and PRV16-2-01 and to COLCIENCIAS by the grant Number 111556933858.

References

  1. 1.
    Ahmed, A.M., et al.: Motor symptoms in Parkinson’s disease: a unified framework. Neurosci. Biobehav. Rev. 68, 727–740 (2016)CrossRefGoogle Scholar
  2. 2.
    Stamford, J.A., Schmidt, P.N., Friedl, K.E.: What engineering technology could do for quality of life in Parkinson’s disease: a review of current needs and opportunities. IEEE J. Biomed. Health Inf. 19(6), 1862–1872 (2015)CrossRefGoogle Scholar
  3. 3.
    Goetz, C.G., et al.: Movement disorder society-sponsored revision of the unified Parkinson’s disease rating scale (mds-updrs): scale presentation and clinimetric testing results. Mov. Disord. 23(15), 2129–2170 (2008)CrossRefGoogle Scholar
  4. 4.
    Rusz, J., et al.: Imprecise vowel articulation as a potential early marker of Parkinson’s disease: effect of speaking task. J. Acoust. Soc. Am. 134(3), 2171–2181 (2013)CrossRefGoogle Scholar
  5. 5.
    Schuller, B., et al.: The INTERSPEECH 2015 computational paralinguistics challenge: nativeness, Parkinson’s & eating condition. In: Proceedings of the 16th INTERSPEECH, pp. 478–482 (2015)Google Scholar
  6. 6.
    Orozco-Arroyave, J.R., Arias-Londoño, J.D., Vargas-Bonil, J.F., González-Rátiva, M.C., Nöth, E.: New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease. In: Proceedings of the 9th LREC, pp. 342–347 (2014)Google Scholar
  7. 7.
    Grósz, T., Róbert, B.-F., Gábor, G., Tóth, L.: Assessing the degree of nativeness and Parkinson’s condition using Gaussian processes and deep rectifier neural networks. In: Proceedings of the 16th INTERSPEECH, pp. 919–923 (2015)Google Scholar
  8. 8.
    Orozco-Arroyave, J.R., et al.: Automatic detection of Parkinson’s disease from words uttered in three different languages. J. Acoust. Soc. Am. 139(1), 481–500 (2016)CrossRefGoogle Scholar
  9. 9.
    Arias-Vergara, T., Vasquez-Correa, J.C., Orozco-Arroyave, J.R., Vargas-Bonilla, J.F., Noth, E.: Parkinson’s disease progression assessment from speech using GMM-UBM. In: Proceedings of the 17th INTERSPEECH, pp. 1933–1937 (2016)Google Scholar
  10. 10.
    Tu, M., Berisha, V., Liss, J.: Objective assessment of pathological speech using distribution regression. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2017)Google Scholar
  11. 11.
    Dehak, N., Kenny, P.J., Dehak, R., Dumouchel, P., Ouellet, P.: Front-end factor analysis for speaker verification. IEEE Trans. Audio Speech Lang. Process. 19(4), 788–798 (2011)CrossRefGoogle Scholar
  12. 12.
    Dehak, N., Torres-Carrasquillo, P.A., Reynolds, D., Dehak, R.: Language recognition via i-vectors and dimensionality reduction. In: Proceedings of the 12th INTERSPEECH, pp. 857–860 (2011)Google Scholar
  13. 13.
    Martínez, D., Burget, L., Ferrer, L., Scheffer, N.: iVector-based prosodic system for language identification. In: Proceedings of the 37th ICASSP, pp. 4861–4864, March 2012Google Scholar
  14. 14.
    Senoussaoui, M., Cardinal, P., Dehak, N., Koerich, A.L.: Native language detection using the i-vector framework. In: Proceedings of the 17th INTERSPEECH, pp. 2398–2402 (2016)Google Scholar
  15. 15.
    Orozco-Arroyave, J.R., et al.: Towards an automatic monitoring of the neurological state of Parkinson’s patients from speech. In: Proceedings of the 41st ICASSP, pp. 6490–6494 (2016)Google Scholar
  16. 16.
    Dehak, N., Dumouchel, P., Kenny, P.: Modeling prosodic features with joint factor analysis for speaker verification. IEEE Trans. Audio Speech Lang. Process. 15(7), 2095–2103 (2007)CrossRefGoogle Scholar
  17. 17.
    Skodda, S., Visser, W., Schlegel, U.: Vowel articulation in Parkinson’s disease. J. Voice 25(4), 467–472 (2012)CrossRefGoogle Scholar
  18. 18.
    Garcia, N., Orozco-Arroyave, J.R., D’Haro, L.F., Dehak, N., Nöth, E.: Evaluation of the neurological state of people with Parkinson’s disease using i-vectors. In: Proceedings of the 18th INTERSPEECH (2017, in Press)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • N. Garcia
    • 1
    Email author
  • J. C. Vásquez-Correa
    • 1
    • 2
  • J. R. Orozco-Arroyave
    • 1
    • 2
  • N. Dehak
    • 3
  • E. Nöth
    • 2
  1. 1.Faculty of EngineeringUniversidad de Antioquia UdeAMedellínColombia
  2. 2.Pattern Recognition LabFriedrich-Alexander-Universität Erlangen-NürnbergErlangenGermany
  3. 3.Center for Language and Speech ProcessingJhons Hopkins UniversityBaltimoreUSA

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