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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    19 Citas (Scopus)


    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.

    Idioma originalInglés
    Número de artículo106248
    PublicaciónComputer Methods and Programs in Biomedicine
    EstadoPublicada - sep. 2021

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    © 2021 Elsevier B.V.

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    • Artículos de investigación con calidad A1 / Q1


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