Shortlist B: a Bayesian model of continuous speech recognition.


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.

DOI: 10.1037/0033-295X.115.2.357

Extracted Key Phrases

18 Figures and Tables

Citations per Year

424 Citations

Semantic Scholar estimates that this publication has 424 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Norris2008ShortlistBA, title={Shortlist B: a Bayesian model of continuous speech recognition.}, author={Dennis Norris and James M. McQueen}, journal={Psychological review}, year={2008}, volume={115 2}, pages={357-95} }