Laryngeal Structure Segmentation in High-Speed Videoendoscopy Using Deep Learning
Laryngeal high-speed videoendoscopy (HSV) offers an effective means of observing the motion of different laryngeal structures along with vibratory behaviors of the vocal folds under various voicing conditions. Segmentation of laryngeal tissues enables analysis of different tissue structures and their dynamics, helping characterize the involvement of laryngeal muscles in voice production. Given the large number of HSV frames, automating this task is imperative. While deep learning-based methods have been implemented in previous studies to segment laryngeal structures, they have not been applied to HSV data during connected speech, which poses significant challenges due to excessive tissue ...
| Authors | Sardar Nafis Bin Ali, Mohsen Zayernouri, Dimitar D. Deliyski et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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