|
Descripción
|
|
|---|---|
| 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. | |
|
Internacional
|
Si |
|
Nombre congreso
|
Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017) |
|
Tipo de participación
|
960 |
|
Lugar del congreso
|
Marrakech, Morocco |
|
Revisores
|
Si |
|
ISBN o ISSN
|
978-3-319-76357-6 |
|
DOI
|
10.1007/978-3-319-76357-6_12 |
|
Fecha inicio congreso
|
11/12/2017 |
|
Fecha fin congreso
|
13/12/2017 |
|
Desde la página
|
120 |
|
Hasta la página
|
129 |
|
Título de las actas
|
Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). In journal Advances in Intelligent Systems and Computing, Volume 737, 2018. ISSN:2194-5357 |