Authors:

NORRIS, D. & McQueen, J.M.

Reference:

Year of publication:

2008

CBU number:

6683

Abstract:

A Bayesian model of continuous speech recognition is presented. It is based on Shortlist (Norris, 1994; Norris, McQueen, Cutler, & 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, however. 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 three 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.