Learning words and their meanings from unsegmented child-directed speech

Most work on language acquisition treats word segmentation-the identification of linguistic segments from continuous speech- and word learning-the mapping of those segments to meanings-as separate problems. These two abilities develop in parallel, however, 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 meanings. The model that learns word meanings proposes more adult-like segmentations for the meaning-bearing words. This result suggests that the non-linguistic context may supply important information for learning word segmentations as well as word meanings.