title: Prediction, Bayesian inference and feedback in speech recognition.
abstract: Speech perception involves prediction, but how is that prediction implemented? In cognitive models prediction has often been taken to imply that there is feedback of activation from lexical to pre-lexical processes as implemented in interactive-activation models (IAMs). We show that simple activation feedback does not actually improve speech recognition. However, other forms of feedback can be beneficial. In particular, feedback can enable the listener to adapt to changing input, and can potentially help the listener to recognise unusual input, or recognise speech in the presence of competing sounds. The common feature of these helpful forms of feedback is that they are all ways of optimising the performance of speech recognition using Bayesian inference. That is, listeners make predictions about speech because speech recognition is optimal in the sense captured in Bayesian models.
authors.affiliation: MRC Cognition and Brain Sciences Unit , Cambridge , UK.
authors.forename: Dennis
authors.initials: D
authors.lastname: Norris
authors.affiliation: Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands.
authors.forename: James M
authors.initials: JM
authors.lastname: McQueen
authors.affiliation: Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands; MARCS Institute, University of Western Sydney, Penrith South, NSW2751, Australia.
authors.forename: Anne
authors.initials: A
authors.lastname: Cutler
keywords: Bayesian inference; Speech recognition; feedback; prediction