X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech

• Published in: Frontiers in neuroinformatics Vol. 15; pp. 578369 - 578369:18
• Main Authors: Jeancolas, Laetitia, Petrovska-Delacrétaz, Dijana, Mangone, Graziella, Benkelfat, Badr-Eddine, Corvol, Jean-Christophe, Vidailhet, Marie, Lehéricy, Stéphane, Benali, Habib
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 19.02.2021; Frontiers Media; Frontiers Media S.A
• Subjects: Alzheimer's disease; automatic detection; Classification; Computer Science; Decision trees; Discriminant analysis; early detection; Gender; Larynx; Life Sciences; Machine Learning; Movement disorders; Neural and Evolutionary Computing; Neural networks; Neurodegenerative diseases; Neurons and Cognition; Neuroscience; Parkinson's disease; Signal and Image Processing; Sound; Speech; Standard deviation; Support vector machines; telediagnosis; voice analysis; x-vectors; x-vectors; Parkinson's disease; telediagnosis; voice analysis; automatic detection; early detection; MFCC; deep neural networks; Deep neural networks; Telediagnosis; X-vectors; Voice analysis; Early detection; Automatic detection

Many articles have used voice analysis to detect Parkinson's disease (PD), but few have focused on the early stages of the disease and the gender effect. In this article, we have adapted the latest speaker recognition system, called x-vectors, in order to detect PD at an early stage using voice...