X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech
نویسندگان
چکیده
Many articles have used voice analysis to detect Parkinson's disease (PD), but few focused on the early stages of and gender effect. In this article, we adapted latest speaker recognition system, called x-vectors, in order PD at an stage using analysis. X-vectors are embeddings extracted from Deep Neural Networks (DNNs), which provide robust representations improve when large amounts training data used. Our goal was assess whether, context detection, technique would outperform more standard classifier MFCC-GMM (Mel-Frequency Cepstral Coefficients—Gaussian Mixture Model) and, if so, under conditions. We recorded 221 French speakers (recently diagnosed subjects healthy controls) with a high-quality microphone via telephone network. Men women were analyzed separately precise models possible Several experimental methodological aspects tested analyze their impacts classification performance. assessed impact audio segment durations, augmentation, type dataset for neural network training, kind speech tasks, back-end analyses. provided better performances than text-independent seemed be particularly suited detection (7–15% improvement). This result observed both recording types (high-quality telephone).
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ژورنال
عنوان ژورنال: Frontiers in Neuroinformatics
سال: 2021
ISSN: ['1662-5196']
DOI: https://doi.org/10.3389/fninf.2021.578369