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Prediction, Bayesian inference and feedback in speech recognition

Norris, D., McQueen, J. M., & Cutler, A. (2016). Prediction, Bayesian inference and feedback in speech recognition. Language, Cognition and Neuroscience, 31(1), 4-18. doi:10.1080/23273798.2015.1081703.
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.
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The Max Planck Institute for Psycholinguistics is an institute of the German Max Planck Society. Our mission is to undertake basic research into the psychological,social and biological foundations of language. The goal is to understand how our minds and brains process language, how language interacts with other aspects of mind, and how we can learn languages of quite different types.

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