Multiple voice disorders in the same individual: Investigating handcrafted features, multi-label classification algorithms, and base-learners

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Autor(es): dc.contributorUniversidade Estadual de Londrina (UEL)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorDhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)-
Autor(es): dc.creatorBarbon, Sylvio-
Autor(es): dc.creatorGuido, Rodrigo Capobianco-
Autor(es): dc.creatorAguiar, Gabriel Jonas-
Autor(es): dc.creatorSantana, Everton José-
Autor(es): dc.creatorProença, Mario Lemes-
Autor(es): dc.creatorPatil, Hemant A.-
Data de aceite: dc.date.accessioned2025-08-21T17:07:40Z-
Data de disponibilização: dc.date.available2025-08-21T17:07:40Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-07-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1016/j.specom.2023.102952-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/304406-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/304406-
Descrição: dc.descriptionNon-invasive acoustic analyses of voice disorders have been at the forefront of current biomedical research. Usual strategies, essentially based on machine learning (ML) algorithms, commonly classify a subject as being either healthy or pathologically-affected. Nevertheless, the latter state is not always a result of a sole laryngeal issue, i.e., multiple disorders might exist, demanding multi-label classification procedures for effective diagnoses. Consequently, the objective of this paper is to investigate the application of five multi-label classification methods based on problem transformation to play the role of base-learners, i.e., Label Powerset, Binary Relevance, Nested Stacking, Classifier Chains, and Dependent Binary Relevance with Random Forest (RF) and Support Vector Machine (SVM), in addition to a Deep Neural Network (DNN) from an algorithm adaptation method, to detect multiple voice disorders, i.e., Dysphonia, Laryngitis, Reinke's Edema, Vox Senilis, and Central Laryngeal Motion Disorder. Receiving as input three handcrafted features, i.e., signal energy (SE), zero-crossing rates (ZCRs), and signal entropy (SH), which allow for interpretable descriptors in terms of speech analysis, production, and perception, we observed that the DNN-based approach powered with SE-based feature vectors presented the best values of F1-score among the tested methods, i.e., 0.943, as the averaged value from all the balancing scenarios, under Saarbrücken Voice Database (SVD) and considering 20% of balancing rate with Synthetic Minority Over-sampling Technique (SMOTE). Finally, our findings of most false negatives for laryngitis may explain the reason why its detection is a serious issue in speech technology. The results we report provide an original contribution, allowing for the consistent detection of multiple speech pathologies and advancing the state-of-the-art in the field of handcrafted acoustic-based non-invasive diagnosis of voice disorders.-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)-
Descrição: dc.descriptionDepartment of Engineering and Architecture University of Trieste, Piazzale Europa, 1 - 34127, FVG-
Descrição: dc.descriptionInstituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth, SP-
Descrição: dc.descriptionComputer Science Department Londrina State University, Rodovia Celso Garcia Cid/PR 445, km 380, Campus Universitário, PR-
Descrição: dc.descriptionSpeech Research Lab Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT)-
Descrição: dc.descriptionInstituto de Biociências Letras e Ciências Exatas Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth, SP-
Descrição: dc.descriptionCAPES: 001-
Descrição: dc.descriptionFAPESP: 2021/12407-4-
Descrição: dc.descriptionCNPq: 303854/2022-7-
Descrição: dc.descriptionCNPq: 310668/2019-0-
Descrição: dc.descriptionCNPq: 420562/2018-4-
Idioma: dc.languageen-
Relação: dc.relationSpeech Communication-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectHandcrafted feature extraction-
Palavras-chave: dc.subjectMulti-label classification-
Palavras-chave: dc.subjectMultiple voice disorders-
Título: dc.titleMultiple voice disorders in the same individual: Investigating handcrafted features, multi-label classification algorithms, and base-learners-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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