Adductor-type Spasmodic Dysphonia is a task-specific focal dystonia characterized by vocal folds' adductor spasms. These involuntary contractions interrupt speech causing strain and strangled voice breaks. The purpose of this paper to is to develop a robust machine learning approach to detect spasmodic dysphonia from voice samples, using balanced data, 10-fold cross validation, and thorough feature selection method based on the Genetic Algorithm. The voice features were analysed using different classifiers such as Naïve-Bayes, Multi-Layer Perceptron, Support Vector Machine, and Random Forest. Statistical analysis was applied to test for significance and superior performance. Results showed that sustained phonation provide higher accuracy models. In addition, Naïve-Bayes outperformed all classifiers with a maximum of 100% and an average of 98.33%. The Genetic Algorithm wrapper feature selection method proved to provide superior performing features than previous researches.

Vocal Test Analysis for the Assessment of Adductor-type Spasmodic Dysphonia / Fayad, R.; Hajj-Hassan, M.; Constantini, G.; Zarazadeh, Z.; Errico, V.; Saggio, G.; Suppa, A.; Asci, F.. - 2021-:(2021), pp. 167-170. (Intervento presentato al convegno 2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME) tenutosi a Werdanyeh, Lebanon) [10.1109/ICABME53305.2021.9604835].

Vocal Test Analysis for the Assessment of Adductor-type Spasmodic Dysphonia

Suppa A.;Asci F.
2021

Abstract

Adductor-type Spasmodic Dysphonia is a task-specific focal dystonia characterized by vocal folds' adductor spasms. These involuntary contractions interrupt speech causing strain and strangled voice breaks. The purpose of this paper to is to develop a robust machine learning approach to detect spasmodic dysphonia from voice samples, using balanced data, 10-fold cross validation, and thorough feature selection method based on the Genetic Algorithm. The voice features were analysed using different classifiers such as Naïve-Bayes, Multi-Layer Perceptron, Support Vector Machine, and Random Forest. Statistical analysis was applied to test for significance and superior performance. Results showed that sustained phonation provide higher accuracy models. In addition, Naïve-Bayes outperformed all classifiers with a maximum of 100% and an average of 98.33%. The Genetic Algorithm wrapper feature selection method proved to provide superior performing features than previous researches.
2021
2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME)
Adductor Spasmodic Dysphonia; Feature extraction; Feature selection; Machine learning; Voice analysis
04 Pubblicazione in atti di convegno::04c Atto di convegno in rivista
Vocal Test Analysis for the Assessment of Adductor-type Spasmodic Dysphonia / Fayad, R.; Hajj-Hassan, M.; Constantini, G.; Zarazadeh, Z.; Errico, V.; Saggio, G.; Suppa, A.; Asci, F.. - 2021-:(2021), pp. 167-170. (Intervento presentato al convegno 2021 Sixth International Conference on Advances in Biomedical Engineering (ICABME) tenutosi a Werdanyeh, Lebanon) [10.1109/ICABME53305.2021.9604835].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1657088
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