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Journal Article

Shortlist B: A Bayesian model of continuous speech recognition


McQueen,  James M.
Language Comprehension Group, MPI for Psycholinguistics, Max Planck Society;
Decoding Continuous Speech, MPI for Psycholinguistics, Max Planck Society;

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Norris, D., & McQueen, J. M. (2008). Shortlist B: A Bayesian model of continuous speech recognition. Psychological Review, 115(2), 357-395. doi:10.1037/0033-295X.115.2.357.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-2001-9
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.