用3D舌体模拟生成可验证的肌肉驱动问答数据集
A Simulator-Grounded Framework For Constructing Verifiable Muscle-Grounded QA From 3D Tongue Meshes (extended version)
- 通过有限元模型将11维肌肉激活映射为舌体网格，生成可追溯的生物力学记录
- 构建89万条高质量问答对，英语与韩语版本经验证一致性达98.6%以上
- 支持跨语言自然化且保持肌肉状态准确性，适合语音建模与认知研究者
现有基于实时MRI和电磁构音仪的构音语料库虽能捕捉舌部运动，但缺乏对每种构型背后肌肉驱动过程的可追溯标注。本文提出一种模拟器基底构建框架，实现3DTongueQA。通过ArtiSynth Badin有限元模型，将受控的11维肌肉激活映射至固定拓扑舌体网格，转化为可复用的生物力学记录，并生成由模拟器定义的肌肉状态、几何形变及目标导向变化的确定性问答对。从295,157个筛选配置中，获得295,115个有效网格，生成每种语言891,156条问答记录。英、韩文自然化仅修改表面形式，且经源记录验证。可替换的SpiralNet++--Qwen3-8B基线在肌肉误差（Muscle EM）上达到$62.9\pm9.2$，在网格随机打乱后降至2.2；锚点保留训练维持全量库存得分的80.4–98.6%，任务特异性读出达$88.7\pm0.7$ Muscle EM与$93.3\pm1.0$ Direction EM。结果表明，几何基底监督可同时支持统一语言问答与结构化预测。代码已公开于https://github.com/esh0504/muscle-grounded-qa。
原文摘要 · Abstract (English)
Existing articulatory corpora based on real-time MRI and electromagnetic articulography capture tongue motion but lack traceable labels for the muscle-driven process underlying each configuration. We introduce a simulator-grounded construction framework, instantiated as 3DTongueQA. Controlled 11-D muscle activations are mapped by the ArtiSynth Badin finite-element model to fixed-topology tongue meshes, converted into reusable biomechanical records, and rendered as deterministic QA on simulator-defined muscle state, geometry, and target-directed change. From 295,157 screened configurations, 295,115 valid meshes yield 891,156 QA records per language. English and Korean naturalizations modify only surface form and are verified against the source records. A swappable SpiralNet++--Qwen3-8B baseline reaches $62.9\pm9.2$ Muscle EM, dropping to 2.2 under mesh shuffling; anchor-held-out training retains 80.4--98.6\% of full-inventory scores, while task-specific readouts reach $88.7\pm0.7$ Muscle EM and $93.3\pm1.0$ Direction EM. The results show geometry-grounded supervision supporting both unified language QA and structured prediction. Code is available at https://github.com/esh0504/muscle-grounded-qa .
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