A key omission from many accounts of children’s early word learning is the linguistic knowledge that the child has acquired up to the point when learning occurs. We simulate this knowledge using a computational model that learns phoneme and word sequence knowledge from naturalistic language corpora. We show how this simple model is able to account for effects of word length, word frequency, neighborhood density and phonotactic probability on children’s early word learning. Moreover, we show how effects of neighborhood density and phonotactic probability on word learning are largely influenced by word length, with our model being able to capture all effects. We then use predictions from the model to show how the ease by which a child learns a new word from maternal input is directly influenced by the phonological knowledge that the child has acquired from other words up to the point of encountering the new word. There are major implications of this work: models and theories of early word learning need to incorporate existing sublexical and lexical knowledge in explaining developmental change while well-established indices of word learning are rejected in favor of phonological knowledge of varying grain sizes.