Statistical and Connectionist Modelling of the Development of Speech Segmentation

Abstract The speech signal is typically continuous; only a minority of word boundaries are marked by any recognizable acoustic cue such as a pause. The continuous nature of speech poses a problem for the adult speaker of the language, in that processing the signal requires a complete parse into words yet any string of more than a few segments is locally multiply ambiguous: given only a phonetic transcription, most words contain other words, in the way that curtain contains cur, or floor contains or. Segmentation strategies are available to the adult listener that are not given to the infant, as the former possesses both a lexicon containing the phonological specification of the words of the language, and a knowledge of the syntax and semantics of the language. For instance, the adult listener may recognize a word before its acoustic offset and hence may be able to predict the end of the current word and the start of the next; indeed, this strategy featured explicitly in one early model of word recognition (Cole and Jakimik 1980 ). The adult listener may be able to recruit syntactic knowledge to predict and identify closed-class words (the short grammatical, or function words) (Shillcock and Bard 1993), and hence identify their boundaries too. The infant, faced with the speech sounds of an unknown language, is unable to draw on such knowledge, yet over the first two years of life individual words are isolated in comprehension, stored and begin to be deployed in production. This chapter is concerned with the nature of the information that the infant might exploit to obtain a foothold on the segmentation problem.