A Connectionist Model for Bootstrap Learning of Syllabic Structure

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Abstract
We report on a series of experiments with simple recurrent networks (SRNs) solving phoneme prediction in continuous phonemic data. The purpose of the experiments is to investigate whether the network output could function as a source for syllable boundary detection. We show that this is possible, using a generalisation of the network resembling the linguistic sonority principle. We argue that the primary generalisation of the network, that is, the fact that sonority varies in a hat-shaped way across phonemic strings, ending and starting at syllable boundaries, is an indication that sonority might be a major cue in discovering the essential building bricks of language when confronted with unsegmented running speech. The segment which is most directly related to sonority patterns, the syllable, has received considerable attention in psycholinguistics as being an element of natural language that is easily grasped by language learners. The phoneme prediction network presents a simulation of the necessary bootstrap to arrive at the discovery of syllabic segmentation in unsegmented speech, which can be used as a basis for the segmentation of larger structures like words.

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