LinguisticsNaomi FeldmanThomas Griffiths, Sharon Goldwater, James Morgan
Bayesian models and artificial language learning tasks show that infant acquiosition of phonetic categories can be helpfully constrained by feedback from word segmentation.Infants segment words from fluent speech during the same period when they are learning phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning by providing information about which sounds occur together in words. Simulations demonstrate that word-level information can successfully disambiguate overlapping English vowel categories. Learning patterns in the model are shown to parallel human behavior from artificial language learning tasks. These findings point to a central role for the developing lexicon in phonetic category acquisition and provide a framework for incorporating top-down constraints into models of category learning.Facultyphonetic category learning70916
