Simulate, record, verify: A language-portable framework for muscle-grounded articulatory QA (extended version)

Abstract

Articulatory corpora from real-time MRI and electromagnetic articulography capture tongue motion but carry no traceable labels for the muscle-driven process behind each configuration, and authoring such supervision by hand, separately for every language, does not scale. We present a simulator-based framework that turns controlled biomechanical inputs into verifiable, language-portable QA supervision. Each simulated configuration is stored with its generating input as a structured fact record; deterministic generators derive gold answers from records alone; and naturalization changes only surface form, with every output checked against its record. A new language therefore needs only a renderer and a lexicon, and new question types need no re-simulation. Instantiated as 3DTongueQA on the ArtiSynth Badin tongue model, 295,115 valid meshes yield 891,156 record-checked QA per language in English and Korean (87.2\% and 88.6\% first-pass verification); a Spanish renderer authored in about 20 minutes reaches 94.1\%, and the checker detects 97--99\% of injected corruptions. The generated supervision is domain-specific: zero-shot GPT-5 Pro reaches 7.2 Muscle EM, whereas a SpiralNet++--Qwen3-8B model trained on it reaches $62.9\pm 9.2$ (2.2 with shuffled meshes) and task-specific readouts reach $88.7\pm 0.7$. Code and templates: https://github.com/esh0504/muscle-grounded-qa.

16 pages, 5 figures, 15 tables