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.