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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| Veröffentlicht in: | Speech communication Jg. 152; S. 102952 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier B.V
01.07.2023
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| ISSN: | 0167-6393, 1872-7182 |
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| Abstract | 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 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.
•The proposed approach detects multiple voice disorders in one individual.•Energy, zero-crossing rates, and entropy were successfully used as features.•Over 90% of accuracy was obtained under SVD database using SMOTE. |
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| AbstractList | 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 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.
•The proposed approach detects multiple voice disorders in one individual.•Energy, zero-crossing rates, and entropy were successfully used as features.•Over 90% of accuracy was obtained under SVD database using SMOTE. |
| ArticleNumber | 102952 |
| Author | Aguiar, Gabriel Jonas Barbon, Sylvio Guido, Rodrigo Capobianco Patil, Hemant A. Santana, Everton José Proença, Mario Lemes |
| Author_xml | – sequence: 1 givenname: Sylvio surname: Barbon fullname: Barbon, Sylvio organization: Department of Engineering and Architecture, University of Trieste, Piazzale Europa, 1 - 34127, Trieste FVG, Italy – sequence: 2 givenname: Rodrigo Capobianco orcidid: 0000-0002-0924-8024 surname: Guido fullname: Guido, Rodrigo Capobianco email: guido@ieee.org organization: Instituto de Biociências, Letras e Ciências Exatas, Unesp - Univ Estadual Paulista (São Paulo State University), Rua Cristóvão Colombo 2265, Jd Nazareth, 15054-000, São José do Rio Preto SP, Brazil – sequence: 3 givenname: Gabriel Jonas surname: Aguiar fullname: Aguiar, Gabriel Jonas organization: Computer Science Department, Londrina State University, Rodovia Celso Garcia Cid/PR 445, km 380, Campus Universitário, Zip code: 86057-970, Londrina PR, Brazil – sequence: 4 givenname: Everton José surname: Santana fullname: Santana, Everton José organization: Computer Science Department, Londrina State University, Rodovia Celso Garcia Cid/PR 445, km 380, Campus Universitário, Zip code: 86057-970, Londrina PR, Brazil – sequence: 5 givenname: Mario Lemes surname: Proença fullname: Proença, Mario Lemes organization: Computer Science Department, Londrina State University, Rodovia Celso Garcia Cid/PR 445, km 380, Campus Universitário, Zip code: 86057-970, Londrina PR, Brazil – sequence: 6 givenname: Hemant A. surname: Patil fullname: Patil, Hemant A. organization: Speech Research Lab, Dhirubhai Ambani Institute of Information and Communication Technology (DA-IICT), Gandhinagar 382007, India |
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| Keywords | Deep learning Multiple voice disorders Handcrafted feature extraction Multi-label classification |
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