Abstract
This paper addresses the estimation of the degree of Parkinson’s Condition (PC) using exclusively the patient’s voice. Firstly, a new database with speech recordings of 25 Spanish patients with different degrees of PC is presented. Secondly, we propose to face this problem as a regression task using machine learning techniques. In particular, utilizing this database, we have developed several systems for predicting the PC degree from a set of acoustic characteristics extracted from the patients’ voice, being the most successful ones, those based on the Support Vector Regression (SVR) algorithm. To determine the optimal way of exploiting the data for our purposes, three kind of experiments have been considered: cross-speaker, leave-one-out-speaker and multi-speaker. From the results, it can be concluded that prediction systems based on acoustic features and machine learning algorithms can be applied for tracking the PC progression if enough validation/training speech data of the patient is available.
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Acknowledgments
The work leading to these results has been partly supported by Spanish Government grants TEC2014-53390-P and DPI2014-53525-C3-2-R. Authors also thank all the patients and the medical staff of the Service of Neurology of the Hospital Universitario Ramón y Cajal involved in the speech data acquisition for their generous contribution to our research (“Estudio sobre clasificación automática de pacientes diagnosticados con Parkinson según la escala UPDRS usando la voz”).
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Jiménez-Recio, C., Zlotnik, A., Gallardo-Antolín, A., Montero, J.M., Martínez-Castrillo, J.C. (2018). Prediction of the Degree of Parkinson’s Condition Using Recordings of Patients’ Voices. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_12
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DOI: https://doi.org/10.1007/978-3-319-76357-6_12
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