Abstract
Two models are presented for generating a representation of words from the input phoneme sequences. They use an unsupervised learning algorithm that compares the input with its internal representation and generates a new representation of each subsequence. Simulation using child-oriented utterances in the CHILDES database as the training stimuli showed that the model performs lexical segmentation better than SRN and that it has fairly good generalization ability.
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