提出非线性发音控制模型的缩放定律，提升可解释性与模拟效率。
Scaling laws for nonlinear dynamical models of articulatory control
- 通过缩放定律解决非线性模型参数化难题
- 在三次模型中实现可解释的发音动态模拟
- 揭示物理与认知约束对语音运动的限制
语音动力学理论借助发音控制计算模型生成定量预测并深化对语音动态的理解。在任务动力学模型中引入非线性恢复力相比线性模型有显著改进，但非线性也带来了参数化与可解释性的挑战。本文通过数值模拟展示这些问题，并提出缩放定律作为解决方案。将缩放定律应用于立方模型，证明其可实现可解释的发音动态模拟，且理论上可被理解为对语音运动动力学模型施加的物理与认知约束。
原文摘要 · Abstract (English)
Dynamical theories of speech use computational models of articulatory control to generate quantitative predictions and advance understanding of speech dynamics. The addition of a nonlinear restoring force to task dynamic models is a significant improvement over linear models, but nonlinearity introduces challenges with parameterization and interpretability. We illustrate these problems through numerical simulations and introduce solutions in the form of scaling laws. We apply the scaling laws to a cubic model and show how they facilitate interpretable simulations of articulatory dynamics, and can be theoretically interpreted as imposing physical and cognitive constraints on models of speech movement dynamics.
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