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PyPhonPlan: Simulating phonetic planning with dynamic neural fields and task dynamics

Sam Kirkham

Abstract

We introduce PyPhonPlan, a Python toolkit for implementing dynamical models of phonetic planning using coupled dynamic neural fields and task dynamic simulations. The toolkit provides modular components for defining planning, perception and memory fields, as well as between-field coupling, gestural inputs, and using field activation profiles to solve tract variable trajectories. We illustrate the toolkit's capabilities through an example application:~simulating production/perception loops with a coupled memory field, which demonstrates the framework's ability to model interactive speech dynamics using representations that are temporally-principled, neurally-grounded, and phonetically-rich. PyPhonPlan is released as open-source software and contains executable examples to promote reproducibility, extensibility, and cumulative computational development for speech communication research.

PyPhonPlan: Simulating phonetic planning with dynamic neural fields and task dynamics

Abstract

We introduce PyPhonPlan, a Python toolkit for implementing dynamical models of phonetic planning using coupled dynamic neural fields and task dynamic simulations. The toolkit provides modular components for defining planning, perception and memory fields, as well as between-field coupling, gestural inputs, and using field activation profiles to solve tract variable trajectories. We illustrate the toolkit's capabilities through an example application:~simulating production/perception loops with a coupled memory field, which demonstrates the framework's ability to model interactive speech dynamics using representations that are temporally-principled, neurally-grounded, and phonetically-rich. PyPhonPlan is released as open-source software and contains executable examples to promote reproducibility, extensibility, and cumulative computational development for speech communication research.
Paper Structure (15 sections, 10 equations, 3 figures)

This paper contains 15 sections, 10 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Model architecture and implementation
  3. Field dynamics
  4. Inputs
  5. Field interactions and thresholds
  6. Memory field
  7. Cross-field coupling
  8. Task dynamics
  9. Application: Coupled perception-planning-memory dynamics
  10. Three-layer field
  11. Simulating phonetic shadowing
  12. Simulation results
  13. Discussion
  14. Conclusion
  15. Acknowledgments

Figures (3)

  • Figure 1: Heatmap of the planning-layer of a DNF with two competing inputs at $x = -5$ and $x = +5$. The red line indicates the start of above-threshold activation; the dotted white line tracks the location of above-threshold peak activation.
  • Figure 2: Memory field coupled to the planning field in Figure \ref{['fig:dnf']}. The memory trace reflects a history of above-threshold planning activation, with a broader spatial profile due to convolution with $w(x-x')$.
  • Figure 3: Shadowing simulation results. Left: planning peak position across baseline with no perceptual input (BL), 10 shadowing trials with strong perceptual input (S1--S10), and washout with no perceptual input (WO). Right: tract variable trajectories for baseline versus washout, showing subtle articulatory consequences of memory-driven convergence.