Connectionist modelling of phonotactic constraints in word recognition

Connectionist techniques for modeling the temporal statistics of phonemically transcribed spoken discourse as described. The aim is to investigate the limits of modeling psycholinguistic data at this prelexical level. The training data respect the frequency with which phoneme strings occur in conventional speech. The general model proposed uses a backpropagation through time learning procedure to train a network that can predict the identity of the phoneme at the next time step, identify the current one, and confirm the last five, after training on noisy data. The model eschews local representations of words and will have implications for current models of word recognition which use such representations.<
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