Machine Learning based Voice Analysis in Spasmodic Dysphonia: An Investigation of Most Relevant Features from Specific Vocal Tasks

Giovanni Costantini, Pietro Di Leo, Francesco Asci, Zakarya Zarezadeh, Luca Marsili, Vito Errico, Antonio Suppa, Antonio Suppa, Giovanni Saggio

2021

Abstract

Adductor-type spasmodic dysphonia (ASD) is a task-specific speech disorder characterized by a strangled and strained voice. We have previously demonstrated that advanced voice analysis, performed with support vector machine, can objectively quantify voice impairment in dysphonic patients, also evidencing results of voice improvements due to symptomatic treatment with botulinum neurotoxin type-A injections into the vocal cords. Here, we expanded the analysis by means of three different machine learning algorithms (Support Vector Machine, Naïve Bayes and Multilayer Percept), on a cohort of 60 ASD patients, some of them also treated with botulinum neurotoxin type A therapy, and 60 age and gender-matched healthy subjects. Our analysis was based on sounds produced by speakers during the emission of /a/ and /e/ sustained vowels and a standardized sentence. As a conclusion, we report the main features with discriminatory capabilities to distinguish untreated vs. treated ASD patients vs. healthy subjects, and a comparison of the three classifiers with respect to their discriminating accuracy.

Paper Citation

in Harvard Style

Costantini G., Di Leo P., Asci F., Zarezadeh Z., Marsili L., Errico V., Suppa A. and Saggio G. (2021). Machine Learning based Voice Analysis in Spasmodic Dysphonia: An Investigation of Most Relevant Features from Specific Vocal Tasks. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS; ISBN 978-989-758-490-9, SciTePress, pages 103-113. DOI: 10.5220/0010344600002865

in Bibtex Style

@conference{biosignals21,
author={Giovanni Costantini and Pietro Di Leo and Francesco Asci and Zakarya Zarezadeh and Luca Marsili and Vito Errico and Antonio Suppa and Giovanni Saggio},
title={Machine Learning based Voice Analysis in Spasmodic Dysphonia: An Investigation of Most Relevant Features from Specific Vocal Tasks},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS},
year={2021},
pages={103-113},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010344600002865},
isbn={978-989-758-490-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2021) - Volume 4: BIOSIGNALS
TI - Machine Learning based Voice Analysis in Spasmodic Dysphonia: An Investigation of Most Relevant Features from Specific Vocal Tasks
SN - 978-989-758-490-9
AU - Costantini G.
AU - Di Leo P.
AU - Asci F.
AU - Zarezadeh Z.
AU - Marsili L.
AU - Errico V.
AU - Suppa A.
AU - Saggio G.
PY - 2021
SP - 103
EP - 113
DO - 10.5220/0010344600002865
PB - SciTePress