[2603.03350] Automated Measurement of Geniohyoid Muscle Thickness During Speech Using Deep Learning and Ultrasound

Quantitative Biology > Quantitative Methods arXiv:2603.03350 (q-bio) [Submitted on 26 Feb 2026] Title:Automated Measurement of Geniohyoid Muscle Thickness During Speech Using Deep Learning and Ultrasound Authors:Alisher Myrgyyassov, Bruce Xiao Wang, Yu Sun, Shuming Huang, Zhen Song, Min Ney Wong, Yongping Zheng View a PDF of the paper titled Automated Measurement of Geniohyoid Muscle Thickness During Speech Using Deep Learning and Ultrasound, by Alisher Myrgyyassov and 6 other authors View PDF HTML (experimental) Abstract:Manual measurement of muscle morphology from ultrasound during speech is time-consuming and limits large-scale studies. We present SMMA, a fully automated framework that combines deep-learning segmentation with skeleton-based thickness quantification to analyze geniohyoid (GH) muscle dynamics. Validation demonstrates near-human-level accuracy (Dice = 0.9037, MAE = 0.53 mm, r = 0.901). Application to Cantonese vowel production (N = 11) reveals systematic patterns: /a:/ shows significantly greater GH thickness (7.29 mm) than /i:/ (5.95 mm, p < 0.001, Cohen's d > 1.3), suggesting greater GH activation during production of /a:/ than /i:/, consistent with its role in mandibular depression. Sex differences (5-8% greater in males) reflect anatomical scaling. SMMA achieves expert-validated accuracy while eliminating the need for manual annotation, enabling scalable investigations of speech motor control and objective assessment of speech and swallowing disorders....