Learning Phonetic Categories by Learning a Lexicon

Infants learn to segment words from fluent speech during the same period as they learn native language phonetic categories, yet accounts of phonetic category acquisition typically ignore information about the words in which speech sounds appear. We use a Bayesian model to illustrate how feedback from segmented words might constrain phonetic category learning, helping a learner disambiguate overlapping phonetic categories. Simulations show that information from an artificial lexicon can successfully disambiguate English vowel categories, leading to more robust category learning than distributional information alone.