Learning words and their meanings from unsegmented child-directed speech

Most work on language acquisition treats word segmentation—the identification of lin-guistic segments from continuous speech— and word learning—the mapping of those seg-ments to meanings—as separate problems. These two abilities develop in parallel, how-ever, raising the question of whether they might interact. To explore the question, we present a new Bayesian segmentation model that incorporates aspects of word learning and compare it to a model that ignores word mean-ings. The model that learns word meanings proposes more adult-like segmentations for the meaning-bearing words. This result sug-gests that the non-linguistic context may sup-ply important information for learning word segmentations as well as word meanings.