Statistical clustering and the contents of the infant vocabulary

Abstract

Infants parse speech into word-sized units according to biases that develop in the first year. One bias, present before the age of 7 months, is to cluster syllables that tend to co-occur. The present computational research demonstrates that this statistical clustering bias could lead to the extraction of speech sequences that are actual words, rather than missegmentations. In English and Dutch, these word-forms exhibit the strong–weak (trochaic) pattern that guides lexical segmentation after 8 months, suggesting that the trochaic parsing bias is learned as a generalization from statistically extracted bisyllables, and not via attention to short utterances or to high-frequency bisyllables. Extracted word-forms come from various syntactic classes, and exhibit distributional characteristics enabling rudimentary sorting of words into syntactic categories. The results highlight the importance of infants’ first year in language learning: though they may know the meanings of very few words, infants are well on their way to building a vocabulary.