Deep Learning for High-Speed Laryngeal Imaging Analysis-Reference-Cited by-同舟云学术

Deep Learning for High-Speed Laryngeal Imaging Analysis

Published:2023-03-09 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)
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Author:

Naghibolhosseini Maryam¹,Yousef Ahmed M¹,Zayernouri Mohsen²,Zacharias Stephanie RC³,Deliyski Dimitar D¹

Affiliation:

1. Michigan State University,Department of Communicative Sciences and Disorders,East Lansing,MI,USA

2. Michigan State University,Department of Mechanical Engineering, and Statistics and Probability,East Lansing,MI,USA

3. Head and Neck Regenerative Medicine Program, Mayo Clinic,Mayo Clinic,Scottsdale Department of Otolaryngology-Head and Neck Surgery,Phoenix,AZ,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10131118/10131680/10131757.pdf?arnumber=10131757

Reference31 articles.

1. A method for analysis of the vocal fold vibrations in connected speech using laryngeal imaging;naghibolhosseini;Proceedings of the 10th International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications MAVEBA,2017

2. Automated detection and segmentation of glottal area using deep-learning neural networks in high-speed videoendoscopy during connected speech;yousef;The 14th International Conference on Advances in Quantitative Laryngology Voice and Speech Research (AQL),2021

3. Temporal Segmentation for Laryngeal High-Speed Videoendoscopy in Connected Speech

4. Integration of transnasal fiberoptic high-speed videoendoscopy with time-synchronized recordings of vocal function;mehta;Normal & Abnormal Vocal Folds Kinematics High Speed Digital Phonoscopy (HSDP) Optical Coherence Tomography (OCT) & Narrow Band Imaging (NBI),2015

5. Studying vocal fold non-stationary behavior during connected speech using high-speed videoendoscopy

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