X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech
Open Access
- 19 February 2021
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Neuroscience
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 and healthy controls) with a high-quality microphone and via the telephone network. Men and women were analyzed separately in order to have more precise models and to assess a possible gender effect. Several experimental and methodological aspects were tested in order to analyze their impacts on classification performance. We assessed the impact of the audio segment durations, data augmentation, type of dataset used for the neural network training, kind of speech tasks, and back-end analyses. X-vectors technique provided better classification performances than MFCC-GMM for the text-independent tasks, and seemed to be particularly suited for the early detection of PD in women (7 to 15 % improvement). This result was observed for both recording types (high-quality microphone and telephone).Keywords
Funding Information
- Concordia University
This publication has 69 references indexed in Scilit:
- Imprecise vowel articulation as a potential early marker of Parkinson's disease: Effect of speaking taskThe Journal of the Acoustical Society of America, 2013
- CLASSIFICATION OF PARKINSON'S DISEASE PATIENTS USING NONLINEAR PHONETIC FEATURES AND MEL-FREQUENCY CEPSTRAL ANALYSISBiomedical Engineering: Applications, Basis and Communications, 2013
- Premotor biomarkers for Parkinson's disease - a promising direction of researchTranslational Neurodegeneration, 2012
- Parkinsons disease Diagnosis using Mel frequency Cepstral Coefficients and Vector QuantizationInternational Journal of Computer Applications, 2011
- Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severityJournal of The Royal Society Interface, 2010
- Automatic Detection of Laryngeal Pathologies in Records of Sustained Vowels by Means of Mel-Frequency Cepstral Coefficient Parameters and Differentiation of Patients by SexFolia Phoniatrica et Logopaedica, 2009
- Gender differences in Parkinson's diseaseJournal of Neurology, Neurosurgery & Psychiatry, 2007
- Speaker Verification Using Adapted Gaussian Mixture ModelsDigital Signal Processing, 2000
- AGEING AND PARKINSON'S DISEASE: SUBSTANTIA NIGRA REGIONAL SELECTIVITYBrain, 1991
- ParkinsonismNeurology, 1967