Name
Title | ||
|---|---|---|
Prediction of the Degree of Parkinson's Condition Using Recordings of Patients' Voices. |
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. |
Year | Venue | Field |
|---|---|---|
2017 | SoCPaR | Regression,Computer science,Support vector machine,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
5 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Clara Jiménez-Recio | 1 | 0 | 0.34 |
| Alexander Zlotnik | 2 | 2 | 1.10 |
| j maciasguarasa | 3 | 92 | 19.30 |
| Juan Manuel Montero | 4 | 218 | 31.51 |
| Juan Carlos Martínez-Castrillo | 5 | 0 | 0.34 |