Machine Learning based Voice Analysis in Spasmodic Dysphonia: An Investigation of Most Relevant Features from Specific Vocal Tasks-Reference-Cited by-同舟云学术

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

Published:2021 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies
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Author:

Costantini Giovanni¹,Di Leo Pietro¹,Asci Francesco²,Zarezadeh Zakarya¹,Marsili Luca³,Errico Vito¹,Suppa Antonio²⁴,Saggio Giovanni¹

Affiliation:

1. Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy, --- Select a Country ---

2. Department of Human Neurosciences, Sapienza University of Rome, 00185 Rome, Italy, --- Select a Country ---

3. Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, OH, U.S.A., --- Select a Country ---

4. IRCCS Neuromed, 86077 Pozzilli (IS), Italy, --- Select a Country ---

Publisher

SCITEPRESS - Science and Technology Publications

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1. Artificial Intelligence Procedure for the Screening of Genetic Syndromes Based on Voice Characteristics;Bioengineering;2023-11-29

2. Voice Disorder Multi-Class Classification for the Distinction of Parkinson’s Disease and Adductor Spasmodic Dysphonia;Applied Sciences;2023-07-25

3. Robust and language-independent acoustic features in Parkinson's disease;Frontiers in Neurology;2023-06-13

4. Machine Learning Assessment of Spasmodic Dysphonia Based on Acoustical and Perceptual Parameters;Bioengineering;2023-03-28

5. Artificial Intelligence-Based Voice Assessment of Patients with Parkinson’s Disease Off and On Treatment: Machine vs. Deep-Learning Comparison;Sensors;2023-02-18