Proceedings of the Annual Meeting of the Cognitive Science Society

The use of transitional probabilities between phonetic segments as a cue for segmenting words from English speech is investigated. W e develop a series of class-based n-gram and feature-based neural network models that enable us to quantify the contribution of low-level statistics to word boundary prediction. Training data for our models is representative of genuine conversational speech: a phonological transcription of the London-Lund corpus. These simple models can be purely bottom-up and hence valid bootstrapping models of infant development. W e go on to demonstrate how the boostrapping models mimic the Metrical Segmentation Strategy of Cutler and Norris (1988), and we discuss the implications of this result.