The secret is in the sound: from unsegmented speech to lexical categories

Abstract

When learning language, young children are faced with many seemingly formidable challenges, including discovering words embedded in a continuous stream of sounds and determining what role these words play in syntactic constructions. We suggest that knowledge of phoneme distributions may play a crucial part in helping children segment words and determine their lexical category, and we propose an integrated model of how children might go from unsegmented speech to lexical categories. We corroborated this theoretical model using a two-stage computational analysis of a large corpus of English child-directed speech. First, we used transition probabilities between phonemes to find words in unsegmented speech. Second, we used distributional information about word edges--the beginning and ending phonemes of words--to predict whether the segmented words from the first stage were nouns, verbs, or something else. The results indicate that discovering lexical units and their associated syntactic category in child-directed speech is possible by attending to the statistics of single phoneme transitions and word-initial and final phonemes. Thus, we suggest that a core computational principle in language acquisition is that the same source of information is used to learn about different aspects of linguistic structure.

Figure 1

The distribution of phoneme transition pairs given the probability of encountering a word boundary between the two phonemes in the corpus of child-directed speech. A probability of 1 indicates that the two phonemes never occur together as a pair inside a word but always straddle a word boundary, whereas a probability of 0 implies that the phoneme pair always occurs inside a word and never are separated by a word boundary.

Figure 2

The completeness (left) and accuracy (right) of classification into lexical categories of the top-5000 lexical candidates from the segmentation procedure using the first and last phoneme in each lexical candidate (white bars) compared with baseline classifications (grey bars - error bars indicate standard error of the mean).