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

Non-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...

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Bibliographische Detailangaben

Veröffentlicht in:	Speech communication Jg. 152; S. 102952
Hauptverfasser:	Barbon, Sylvio, Guido, Rodrigo Capobianco, Aguiar, Gabriel Jonas, Santana, Everton José, Proença, Mario Lemes, Patil, Hemant A.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.07.2023
Schlagworte:	Deep learning Handcrafted feature extraction Multi-label classification Multiple voice disorders Deep learning Multiple voice disorders Handcrafted feature extraction Multi-label classification
ISSN:	0167-6393, 1872-7182
Online-Zugang:	Volltext
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