Phonetic relevance and phonemic grouping of speech in the automatic detection of parkinson’s Disease

Moro Velázquez, Laureano and Gomez Garcia, Jorge Andres and Godino Llorente, Juan Ignacio and Grandas Pérez, Francisco and Shattuck-Hufnagel, Stefanie and Yagüe Jiménez, Virginia and Dehak, Najim (2019). Phonetic relevance and phonemic grouping of speech in the automatic detection of parkinson’s Disease. "Scientific Reports", v. 9 (n. 19066); pp. 1-16. ISSN 2045-2322. https://doi.org/10.1038/s41598-019-55271-y.

Description

Title: Phonetic relevance and phonemic grouping of speech in the automatic detection of parkinson’s Disease
Author/s:
  • Moro Velázquez, Laureano
  • Gomez Garcia, Jorge Andres
  • Godino Llorente, Juan Ignacio
  • Grandas Pérez, Francisco
  • Shattuck-Hufnagel, Stefanie
  • Yagüe Jiménez, Virginia
  • Dehak, Najim
Item Type: Article
Título de Revista/Publicación: Scientific Reports
Date: 13 December 2019
ISSN: 2045-2322
Volume: 9
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Otro
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Literature documents the impact of Parkinson?s Disease (PD) on speech but no study has analyzed in detail the importance of the distinct phonemic groups for the automatic identifcation of the disease. This study presents new approaches that are evaluated in three diferent corpora containing speakers sufering from PD with two main objectives: to investigate the infuence of the diferent phonemic groups in the detection of PD and to propose more accurate detection schemes employing speech. The proposed methodology uses GMM-UBM classifers combined with a technique introduced in this paper called phonemic grouping, that permits observation of the diferences in accuracy depending on the manner of articulation. Cross-validation results reach accuracies between 85% and 94% with AUC ranging from 0.91 to 0.98, while cross-corpora trials yield accuracies between 75% and 82% with AUC between 0.84 and 0.95, depending on the corpus. This is the frst work analyzing the generalization properties of the proposed approaches employing cross-corpora trials and reaching high accuracies. Among the diferent phonemic groups, results suggest that plosives, vowels and fricatives are the most relevant acoustic segments for the detection of PD with the proposed schemes. In addition, the use of text-dependent utterances leads to more consistent and accurate models.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainDPI2017-83405-RUnspecifiedUnspecifiedUnspecified

More information

Item ID: 64439
DC Identifier: http://oa.upm.es/64439/
OAI Identifier: oai:oa.upm.es:64439
DOI: 10.1038/s41598-019-55271-y
Official URL: https://www.nature.com/articles/s41598-019-55271-y
Deposited by: Memoria Investigacion
Deposited on: 11 Oct 2020 09:47
Last Modified: 11 Oct 2020 15:58
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