Comparison of telephone recordings and professional microphone recordings for early detection of Parkinson's disease, using mel-frequency cepstral coefficients with Gaussian mixture models
In: Interspeech 2019, 2019
Online
Konferenz
Zugriff:
International audience ; Vocal impairments are among the earliest symptoms in Parkinson's Disease (PD). We adapted a method classically used in speech and speaker recognition, based on Mel-Frequency Cepstral Coefficients (MFCC) extraction and Gaussian Mixture Model (GMM) to detect recently diagnosed and pharmacologically treated PD patients. We classified early PD subjects from controls with an accuracy of 83%, using recordings obtained with a professional microphone. More interestingly, we were able to classify PD from controls with an accuracy of 75 % based on telephone recordings. As far as we know, this is the first time that audio recordings from telephone network have been used for early PD detection. This is a promising result for a potential future telediagnosis of Parkinson's disease.
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Comparison of telephone recordings and professional microphone recordings for early detection of Parkinson's disease, using mel-frequency cepstral coefficients with Gaussian mixture models
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| Autor/in / Beteiligte Person: | Jeancolas, Laetitia ; Mangone, Graziella ; Corvol, Jean-Christophe ; Vidailhet, Marie ; Lehéricy, Stéphane ; Benkelfat, Badr-Eddine ; Benali, Habib ; Petrovska-Delacrétaz, Dijana ; Institut Polytechnique de Paris (IP Paris) ; Département Electronique et Physique (TSP - EPH) ; Institut Mines-Télécom Paris (IMT)-Télécom SudParis (TSP) ; (ARMEDIA-SAMOVAR), ARMEDIA ; Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux (SAMOVAR) ; Institut Mines-Télécom Paris (IMT)-Télécom SudParis (TSP)-Institut Mines-Télécom Paris (IMT)-Télécom SudParis (TSP) ; Centre National de la Recherche Scientifique (CNRS) ; Institut du Cerveau = Paris Brain Institute (ICM) ; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP ; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) ; CHU Pitié-Salpêtrière AP-HP ; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU) ; Centre d'investigation clinique Neurosciences CHU Pitié Salpêtrière (CIC Neurosciences) ; Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière AP-HP ; Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU) ; Traitement de l'Information Pour Images et Communications (TIPIC-SAMOVAR) ; centre, PERFORM ; Concordia University Montreal |
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| Zeitschrift: | Interspeech 2019, 2019 |
| Veröffentlichung: | HAL CCSD ; International Speech Communication Association (ISCA), 2019 |
| Medientyp: | Konferenz |
| DOI: | 10.21437/Interspeech.2019-2825 |
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