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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.03350 |
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| _version_ | 1866918368786776064 |
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| author | Myrgyyassov, Alisher Wang, Bruce Xiao Sun, Yu Huang, Shuming Song, Zhen Wong, Min Ney Zheng, Yongping |
| author_facet | Myrgyyassov, Alisher Wang, Bruce Xiao Sun, Yu Huang, Shuming Song, Zhen Wong, Min Ney Zheng, Yongping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03350 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automated Measurement of Geniohyoid Muscle Thickness During Speech Using Deep Learning and Ultrasound Myrgyyassov, Alisher Wang, Bruce Xiao Sun, Yu Huang, Shuming Song, Zhen Wong, Min Ney Zheng, Yongping Quantitative Methods Machine Learning Sound Audio and Speech Processing 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. |
| title | Automated Measurement of Geniohyoid Muscle Thickness During Speech Using Deep Learning and Ultrasound |
| topic | Quantitative Methods Machine Learning Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2603.03350 |