A model of generalization in distributional learning of phonetic categories

Computational work in the past decade has produced several models accounting for phonetic category learning from distributional and lexical cues. However, there have been no computational proposals for how people might use another powerful learning mechanism: generalization from learned to analogous distinctions (e.g., from /b/–/p/ to /g/–/k/). Here, we present a new simple model of generalization in phonetic category learning, formalized in a hierarchical Bayesian framework. The model captures our proposal that linguistic knowledge includes the possibility that category types in a language (such as voiced and voiceless) can be shared across sound classes (such as labial and velar), thus naturally leading to generalization. We present two sets of simulations that reproduce key features of human performance in behavioral experiments, and we discuss the model’s implications and directions for future research.