Machine Learning-based Study of Dysphonic Voices for the Identification and Differentiation of Vocal Cord Paralysis and Vocal Nodules

Valerio Cesarini, Carlo Robotti, Ylenia Piromalli, Francesco Mozzanica, Antonio Schindler, Giovanni Saggio, Giovanni Costantini

2022

Abstract

Dysphonia can be caused by multiple different conditions, which are often indistinguishable through perceptual evaluation, even when undertaken by experienced clinicians. Furthermore, definitive diagnoses are often not immediate and performed only in clinical settings through laryngoscopy, which is an invasive procedure. This study took into account Vocal Cord Paralysis (VCP) and Vocal Nodules (VN) given their perceptual similarity and, with the aid of euphonic control subjects, aimed to build a framework for the identification and differentiation of the diseases. A dataset of voice recordings comprised of 87 control subjects, 85 subjects affected by VN, and 120 subjects affected by VCP was carefully built within a controlled clinical setting. A Machine-Learning framework was built, based on a correlation-based feature selection bringing relevant biomarkers, followed by a ranker and a Gaussian Support Vector Machine (SVM) classifier. The results of the classifications were promising, with the comparisons versus healthy subjects bringing accuracies higher than 98%, while 89.21% was achieved for the differentiation. This suggests that it may be possible to automatically identify dysphonic voices, differentiating etiologies of dysphonia. The selected biomarkers further validate the analysis highlighting a trend of poor volume control in dysphonic subjects, while also refining the existing literature.

Paper Citation

in Harvard Style

Cesarini V., Robotti C., Piromalli Y., Mozzanica F., Schindler A., Saggio G. and Costantini G. (2022). Machine Learning-based Study of Dysphonic Voices for the Identification and Differentiation of Vocal Cord Paralysis and Vocal Nodules. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS; ISBN 978-989-758-552-4, SciTePress, pages 265-272. DOI: 10.5220/0010913800003123

in Bibtex Style

@conference{biosignals22,
author={Valerio Cesarini and Carlo Robotti and Ylenia Piromalli and Francesco Mozzanica and Antonio Schindler and Giovanni Saggio and Giovanni Costantini},
title={Machine Learning-based Study of Dysphonic Voices for the Identification and Differentiation of Vocal Cord Paralysis and Vocal Nodules},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS},
year={2022},
pages={265-272},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010913800003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 4: BIOSIGNALS
TI - Machine Learning-based Study of Dysphonic Voices for the Identification and Differentiation of Vocal Cord Paralysis and Vocal Nodules
SN - 978-989-758-552-4
AU - Cesarini V.
AU - Robotti C.
AU - Piromalli Y.
AU - Mozzanica F.
AU - Schindler A.
AU - Saggio G.
AU - Costantini G.
PY - 2022
SP - 265
EP - 272
DO - 10.5220/0010913800003123
PB - SciTePress