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

ABSTRACT: 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 analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust speaker representations and improve speaker recognition when large amounts of training data are used. Our goal was to assess whether, in the context of early PD detection, this technique would outperform the more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients-Gaussian Mixture Model) and, if so, under which conditions. We recorded 221 French speakers (recently diagnosed PD subjects a

SUBMITTER: Jeancolas L 

PROVIDER: S-EPMC7935511 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature