title: Shortlist B: a Bayesian model of continuous speech recognition.
abstract: A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (D. Norris, 1994; D. Norris, J. M. McQueen, A. Cutler, & S. Butterfield, 1997) and shares many of its key assumptions: parallel competitive evaluation of multiple lexical hypotheses, phonologically abstract prelexical and lexical representations, a feedforward architecture with no online feedback, and a lexical segmentation algorithm based on the viability of chunks of the input as possible words. Shortlist B is radically different from its predecessor in two respects. First, whereas Shortlist was a connectionist model based on interactive-activation principles, Shortlist B is based on Bayesian principles. Second, the input to Shortlist B is no longer a sequence of discrete phonemes; it is a sequence of multiple phoneme probabilities over 3 time slices per segment, derived from the performance of listeners in a large-scale gating study. Simulations are presented showing that the model can account for key findings: data on the segmentation of continuous speech, word frequency effects, the effects of mispronunciations on word recognition, and evidence on lexical involvement in phonemic decision making. The success of Shortlist B suggests that listeners make optimal Bayesian decisions during spoken-word recognition.
authors.affiliation: Medical Research Council, Cognition and Brain Sciences Unit, Cambridge, UK. dennis.norris@mrc-cbu.cam.ac.uk
authors.forename: Dennis
authors.initials: D
authors.lastname: Norris
authors.forename: James M
authors.initials: JM
authors.lastname: McQueen
mesh_terms: D001499:Bayes Theorem; D006801:Humans; D021641:Recognition, Psychology; D013067:Speech Perception; D014705:Verbal Behavior