PyPhonPlan: Simulating phonetic planning with dynamic neural fields and task dynamics
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Phonetic accommodation and inhibition in a dynamic neural field model
Short-term phonetic accommodation is a fundamental driver behind accent change, but how does real-time input from another speaker's voice shape the speech planning representations of an interlocutor? We advance a computa…
Learning rigid dynamics with face interaction graph networks
Simulating rigid collisions among arbitrary shapes is notoriously difficult due to complex geometry and the strong non-linearity of the interactions. While graph neural network (GNN)-based models are effective at learnin…
Graph Neural NetworkBayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of Stanene
We present a way to dramatically accelerate Gaussian process models for interatomic force fields based on many-body kernels by mapping both forces and uncertainties onto functions of low-dimensional features. This allows…
Active LearningNeural inhibition during speech planning contributes to contrastive hyperarticulation
Previous work has demonstrated that words are hyperarticulated on dimensions of speech that differentiate them from a minimal pair competitor. This phenomenon has been termed contrastive hyperarticulation (CH). We presen…
Simulating Coverage Path Planning with Roomba
Coverage Path Planning involves visiting every unoccupied state in an environment with obstacles. In this paper, we explore this problem in environments which are initially unknown to the agent, for purposes of simulatin…
Deep Reinforcement Learningreinforcement-learningReinforcement Learning (RL)