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Merging information in speech recognition: Feedback is never necessary

Published online by Cambridge University Press:  10 October 2000

Dennis Norris
Affiliation:
Medical Research Council Cognition and Brain Sciences Unit, Cambridge, CB2 2EF, United Kingdom dennis.norris@.mrc-cbu.cam.ac.uk www.mrc-cbu.cam.ac.uk
James M. McQueen
Affiliation:
Max-Planck-Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands james.mcqueen@mpi.nl www.mpi.nl anne.cutler@mpi.nl www.mpi.nl
Anne Cutler
Affiliation:
Max-Planck-Institute for Psycholinguistics, 6525 XD Nijmegen, The Netherlands james.mcqueen@mpi.nl www.mpi.nl anne.cutler@mpi.nl www.mpi.nl

Abstract

Top-down feedback does not benefit speech recognition; on the contrary, it can hinder it. No experimental data imply that feedback loops are required for speech recognition. Feedback is accordingly unnecessary and spoken word recognition is modular. To defend this thesis, we analyse lexical involvement in phonemic decision making. TRACE (McClelland & Elman 1986), a model with feedback from the lexicon to prelexical processes, is unable to account for all the available data on phonemic decision making. The modular Race model (Cutler & Norris 1979) is likewise challenged by some recent results, however. We therefore present a new modular model of phonemic decision making, the Merge model. In Merge, information flows from prelexical processes to the lexicon without feedback. Because phonemic decisions are based on the merging of prelexical and lexical information, Merge correctly predicts lexical involvement in phonemic decisions in both words and nonwords. Computer simulations show how Merge is able to account for the data through a process of competition between lexical hypotheses. We discuss the issue of feedback in other areas of language processing and conclude that modular models are particularly well suited to the problems and constraints of speech recognition.

Type
Research Article
Copyright
© 2000 Cambridge University Press

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