A Bayesian belief updating model of phonetic recalibration and selective adaptation

Abstract

The mapping from phonetic categories to acoustic cue values is highly flexible, and adapts rapidly in response to exposure. There is currently, however, no theoretical framework which captures the range of this adaptation. We develop a novel approach to modeling phonetic adaptation via a belief-updating model, and demonstrate that this model naturally unifies two adaptation phenomena traditionally considered to be distinct.