Speaker Model to Monitor the Neurological State and the Dysarthria Level of Patients with Parkinson’s Disease
The progression of the disease in Parkinson’s patients is commonly evaluated with the unified Parkinson’s disease rating scale (UPDRS), which contains several items to assess motor and non–motor impairments. The patients develop speech impairments that can be assessed with a scale to evaluate dysarthria. Continuous monitoring of the patients is suitable to update the medication or the therapy. In this study, a robust speaker model based on the GMM–UBM approach is proposed for the continuous monitoring of the state of Parkinson’s patients. The model is trained with phonation, articulation, and prosody features with the aim of evaluating deficits on each speech dimension. The performance of the model is evaluated in two scenarios: the monitoring of the UPDRS score and the prediction of the dysarthria level of the speakers. The results indicate that the speaker models are suitable to track the disease progression, specially in terms of the evaluation of the dysarthia level of the speakers.
KeywordsParkinson’s disease UPDRS Dysarthria Phonation Articulation Prosody Speaker model
The work reported here was started at JSALT 2016, and was supported by JHU via grants from DARPA (LORELEI), Microsoft, Amazon, Google and Facebook. Thanks also to CODI from University of Antioquia by the grant Numbers 2015–7683 and PRV16-2-01.
- 4.Enderby, P.M., Palmer, R.: FDA-2: Frenchay Dysarthria Assessment: Examiner’s Manual. Pro-Ed, Texas (2008)Google Scholar
- 7.Gómez-Vilda, P., Vicente-Torcal, M.C., Ferrández-Vicente, J.M., Álvarez-Marquina, A., Rodellar-Biarge, V., Nieto-Lluis, V., Martínez-Olalla, R.: Parkinson’s disease monitoring from phonation biomechanics. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Toledo-Moreo, F.J., Adeli, H. (eds.) IWINAC 2015. LNCS, vol. 9107, pp. 238–248. Springer, Cham (2015). doi: 10.1007/978-3-319-18914-7_25 CrossRefGoogle Scholar
- 8.Arias-Vergara, T., Vásquez-Correa, J.C., Orozco-Arroyave, J.R., Vargas-Bonilla, J.F., Nöth, E.: Parkinson disease progression assessment from speech using GMM-UBM. In: Annual Conference of the International Speech Communication Association (INTERSPEECH), pp. 1933–1937 (2016)Google Scholar
- 9.Nöth, E., et al.: Remote monitoring of neurodegeneration through speech. In: Final Presentation of the Third Frederick Jelinek Memorial Summer Workshop (JSALT), August 2016Google Scholar
- 10.Vásquez-Correa, J.C., Orozco-Arroyave, J.R., Arora, R., Nöth, E., Dehak, N., Christensen, H., Rudzicz, F., Bocklet, T., Cernak, M., Chinaei, H., et al.: Multi-view representation learning via GCCA for multimodal analysis of Parkinson’s disease. In: 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2017) (2017)Google Scholar
- 11.Orozco-Arroyave, J.R., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Gonzalez-Rátiva, M.C., Nöth, E.: New Spanish speech corpus database for the analysis of people suffering from Parkinson’s disease. In: Language Resources and Evaluation Conference, (LREC), pp. 342–347 (2014)Google Scholar
- 12.Orozco-Arroyave, J.R., Vásquez-Correa, J.C., Hönig, F., Arias-Londoño, J.D., Vargas-Bonilla, J.F., Skodda, S., Rusz, J., Nöth, E.: Towards an automatic monitoring of the neurological state of the Parkinson’s patients from speech. In: 41st International Conference on Acoustic, Speech, and Signal Processing (ICASSP), pp. 6490–6494 (2016)Google Scholar