A Deep Learning Approach for Quantifying Vocal Fold Dynamics During Connected Speech Using Laryngeal High-Speed Videoendoscopy

In brief

This study presents a deep learning framework using convolutional neural networks to automatically quantify vocal fold movement patterns from high-speed videoendoscopy during connected speech. The approach enables objective assessment of voice disorders by analyzing vocal fold dynamics with high temporal resolution, potentially improving diagnostic accuracy and clinical workflow efficiency.

Written by the GCMD Library team from the article.