Machine learning based analysis of speech dimensions in functional oropharyngeal dysphagia

Sebastian Roldan-Vasco, Andres Orozco-Duque, Juan Camilo Suarez-Escudero, Juan Rafael Orozco-Arroyave

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    4 Scopus citations

    Abstract

    Background and objective: The normal swallowing process requires a complex coordination of anatomical structures driven by sensory and cranial nerves. Alterations in such coordination cause swallowing malfunctions, namely dysphagia. The dysphagia screening methods are quite subjective and experience dependent. Bearing in mind that the swallowing process and speech production share some anatomical structures and mechanisms of neurological control, this work aims to evaluate the suitability of automatic speech processing and machine learning techniques for screening of functional dysphagia. Methods: Speech recordings were collected from 46 patients with functional oropharyngeal dysphagia produced by neurological causes, and 46 healthy controls. The dimensions of speech including phonation, articulation, and prosody were considered through different speech tasks. Specific features per dimension were extracted and analyzed using statistical tests. Machine learning models were applied per dimension via nested cross-validation. Hyperparameters were selected using the AUC - ROC as optimization criterion. Results: The Random Forest in the articulation related speech tasks retrieved the highest performance measures (AUC=0.86±0.10, sensitivity=0.91±0.12) for individual analysis of dimensions. In addition, the combination of speech dimensions with a voting ensemble improved the results, which suggests a contribution of information from different feature sets extracted from speech signals in dysphagia conditions. Conclusions: The proposed approach based on speech related models is suitable for the automatic discrimination between dysphagic and healthy individuals. These findings seem to have potential use in the screening of functional oropharyngeal dysphagia in a non-invasive and inexpensive way.

    Original languageEnglish
    Article number106248
    JournalComputer Methods and Programs in Biomedicine
    Volume208
    DOIs
    StatePublished - Sep 2021

    Bibliographical note

    Funding Information:
    This work has been supported by COLCIENCIAS - República de Colombia, research project No. 121077758144. This work has been also partitally supported by CODI at Universidad de Antioquia, grant # PRG2017-15530. Also, authors thank the Organización Fonoaudiológica E.U. (OFA IPS) for help with patients recruitment and clinical evaluation.

    Funding Information:
    This work has been supported by COLCIENCIAS - República de Colombia, research project No. 121077758144. This work has been also partitally supported by CODI at Universidad de Antioquia, grant # PRG2017-15530. Also, authors thank the Organización Fonoaudiológica E.U. (OFA IPS) for help with patients recruitment and clinical evaluation.

    Publisher Copyright:
    © 2021 Elsevier B.V.

    Keywords

    • Dysphagia
    • Feature extraction
    • Machine learning
    • Speech analysis
    • Speech processing
    • Voice changes

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