PyPhonPlan: Simulating phonetic planning with dynamic neural fields and task dynamics
This toolkit addresses the need for reproducible and extensible computational models in speech communication research, though it is incremental as it builds on existing dynamical modeling approaches.
The authors introduced PyPhonPlan, a Python toolkit for simulating phonetic planning using dynamic neural fields and task dynamics, enabling modeling of interactive speech dynamics with temporally-principled and phonetically-rich representations.
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.