Analysis of phonatory features for the automatic detection of Parkinson's Disease in two different corpora

Moro Velázquez, Laureano and Gómez García, Jorge Andrés and Najim, Dehak and Godino Llorente, Juan Ignacio (2019). Analysis of phonatory features for the automatic detection of Parkinson's Disease in two different corpora. In: "Models and analysis of vocal emissions for biomedical applications : 11th International Workshop (MAVEBA 2019)", 17/12/2019 - 19/12/2019, Firenze, Italy. pp. 33-36.

Description

Title: Analysis of phonatory features for the automatic detection of Parkinson's Disease in two different corpora
Author/s:
  • Moro Velázquez, Laureano
  • Gómez García, Jorge Andrés
  • Najim, Dehak
  • Godino Llorente, Juan Ignacio
Item Type: Presentation at Congress or Conference (Article)
Event Title: Models and analysis of vocal emissions for biomedical applications : 11th International Workshop (MAVEBA 2019)
Event Dates: 17/12/2019 - 19/12/2019
Event Location: Firenze, Italy
Title of Book: Models and analysis of vocal emissions for biomedical applications : 11th International Workshop (MAVEBA 2019)
Date: 2019
Subjects:
Freetext Keywords: Parkinson’s Disease; Modulation Spectrum; GMM; Complexity; Noise
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Teoría de la Señal y Comunicaciones
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In this study, several automatic detectors of Parkinson's Disease (PD) based on phonatory aspects were analyzed employing two different corpora containing speech from speakers with PD. The features employed to characterize phonation were jitter, shimmer, noise measurements, complexity, modulation spectrum features and perceptual linear predictive coefficients. To differentiate between speakers with and without PD a gaussian mixture model classification scheme was used. Then, the approach providing the best results was combined with a scheme using articulatory information of the speech in order to assess the complementarity between phonatory and articulatory aspects in the automatic detection of PD. Cross-validation trials (k-folds) employing exclusively phonatory information provided accuracies between 64% and 71%, with AUC between 0.68 and 0.80 depending on the corpus. Results suggest that a combination of all the analyzed features with a PCA dimensionality reduction produce the best accuracy, AUC and sensibility. Also, results indicate that phonatory approaches tend to be less accurate in PD detection than other articulatory approaches proposed in previous studies. Finally, results suggest the complementarity between the studied articulatory and phonatory approaches is low.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainDPI2017-83405-RUnspecifiedUnspecifiedUnspecified

More information

Item ID: 65329
DC Identifier: https://oa.upm.es/65329/
OAI Identifier: oai:oa.upm.es:65329
Deposited by: Memoria Investigacion
Deposited on: 24 Apr 2021 06:07
Last Modified: 24 Apr 2021 06:07
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