Scaling laws for nonlinear dynamical models of articulatory control

TL;DR

This paper introduces scaling laws for nonlinear dynamical models of speech articulatory control, improving interpretability and enabling better simulation of speech dynamics by imposing physical and cognitive constraints.

Contribution

The paper develops and applies scaling laws to nonlinear models, addressing parameterization and interpretability challenges in speech dynamics modeling.

Findings

Scaling laws facilitate interpretable simulations.

They impose physical and cognitive constraints.

Applied to a cubic model, they improve understanding of speech movement dynamics.

Abstract

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.