← Back to all news
arXiv Computer Vision By Sardar Nafis Bin Ali, Mohsen Zayernouri, Dimitar D. Deliyski, Maryam Naghibolhosseini

Laryngeal Structure Segmentation in High-Speed Videoendoscopy Using Deep Learning

Read the original on arXiv Computer Vision →

The study presents deep learning‑based segmentation of laryngeal structures in high‑speed videoendoscopy (HSV) data, including connected speech. Using U‑Net models, the authors trained networks to detect aryepiglottic folds, arytenoid cartilages, vocal folds, epiglottis, and glottal area from HSV recordings of both sustained vowel phonation and connected speech in normal and disordered voices. After applying noise removal and histogram equalization, the networks achieved overall accuracies above 95%, demonstrating reliable automated analysis of laryngeal dynamics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

Related stories

arXiv Machine Learning
Sep 29

BreathGRU: A Novel Semi-Supervised Bidirectional Gated Recurrent Unit Framework for Speech and Breath Segmentation for Respiratory Audio

BreathGRU is a semi‑supervised Bidirectional Gated Recurrent Unit framework designed to segment speech and breath events in respiratory audio. It combines acoustic feature extraction, bidirectional recurrent modeling, pseudo‑label refinement, and duration‑constrained Segmental Viterbi decoding to produce accurate speech‑breath segmentation. In evaluations against existing methods, BreathGRU achieved the highest breath event recall, lowest onset‑localisation error, and highest Mean Match Intersection over Union, outperforming large pretrained VAD models such as Silero.

By Sania Fatima Sayed, John W. Holloway, Reyer Zwiggelaar, Faisal I. Rezwan
computer-vision
More like this →
arXiv Machine Learning
2d ago

Self-Supervised Speech Representations for Cross-Speaker Dysarthria Detection During Awake Craniotomy

arXiv:2610.11825v1 Announce Type: new Abstract: Detecting intra-operative speech impairment during awake craniotomy is essential for preserving language function. However, automated detection remains...

By Kanthila Chinmayi (IRDL, LaTIM), Abdallah Nassib (LARIS), Misy Harrison (LaTIM), Panheleux Celine (LaTIM, CHU - BREST), Saliou Vanessa (CHU - BREST), Seizeur Romuald (LaTIM), Dardenne Guillaume (LaTIM)
rag
More like this →