RAW: a real-speech model for human word recognition

Abstract

Computational models have become more and more important in testing processing mechanisms assumed to underlie human spoken-word recognition. Models like TRACE (McClelland and Elman, 1986) and Shortlist (Norris, 1994) have given us much insight in the effects of, for instance, competition between words in the mental lexicon and the use of lexical information during word recognition. However, these models neglect the effects of coarticulation and variability over time by using mock speech instead of real speech input. We describe a new connectionist model for spoken-word recognition which differs on a number of points from other models, in that it takes real speech as input, is based on a new architecture for the representation of time, and can adapt its own weights. Simulations with the model accurately reproduce some important effects found in human word recognition. However, the representations of words in the model and the implementation of the frequency effect should be investigated more thoroughly.