Phonetic accommodation and inhibition in a dynamic neural field model

TL;DR

This paper presents a computational neural field model explaining how real-time speech input influences phonetic adaptation, predicting convergence and divergence patterns in speech planning influenced by inhibitory memory dynamics.

Contribution

It introduces a dynamic neural field model that captures the mechanisms of phonetic accommodation and inhibition, linking neural dynamics to observable speech adaptation phenomena.

Findings

Model predicts convergence and divergence in speech planning.

Empirical data supports the role of inhibitory memory dynamics.

Variation in inhibitory strength may explain resistance to accommodation.

Abstract

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 computational model of change in speech planning representations during phonetic accommodation, grounded in dynamic neural field equations for movement planning and memory dynamics. A dual-layer planning/memory field predicts that convergence to a model talker on one trial can trigger divergence on subsequent trials, due to a delayed inhibitory effect in the more slowly evolving memory field. The model's predictions are compared with empirical patterns of accommodation from an experimental pilot study. We show that observed empirical phenomena may correspond to variation in the magnitude of inhibitory memory dynamics, which could reflect resistance to accommodation due to phonological and/or sociolinguistic pressures. We discuss the implications of these results for the relations between short-term phonetic accommodation and sound change.