Comprehension without segmentation: a proof of concept with naive discriminative learning

Current theories of auditory comprehension assume that the segmentation of speech into word forms is an essential prerequisite to understanding. We present a computational model that does not seek to learn word forms, but instead decodes the experiences discriminated by the speech input. At the heart of this model is a discrimination learning network trained on full utterances. This network constitutes an atemporal long-term memory system. A fixed-width short-term memory buffer projects a constantly updated moving window over the incoming speech onto the network's input layer. In response, the memory generates temporal activation functions for each of the output units. We show that this new discriminative perspective on auditory comprehension is consistent with young infants' sensitivity to the statistical structure of the input. Simulation studies, both with artificial language and with English child-directed speech, provide a first computational proof of concept and demonstrate the importance of utterance-wide co-learning.