Scaling laws for nonlinear dynamical models of articulatory control
This work addresses interpretability issues in speech dynamics modeling for researchers, but it is incremental as it builds on existing nonlinear models.
The authors tackled the challenges of parameterization and interpretability in nonlinear dynamical models of articulatory control by introducing scaling laws, which they applied to a cubic model to enable interpretable simulations and impose physical and cognitive constraints on speech movement dynamics.
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.