Prediction, Bayesian inference and feedback in speech recognition

Dennis Norris, James M. McQueen, Anne Cutler

    Research output: Contribution to journalArticlepeer-review

    100 Citations (Scopus)

    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.
    Original languageEnglish
    Pages (from-to)4-18
    Number of pages15
    JournalLanguage, Cognition and Neuroscience
    Volume31
    Issue number1
    DOIs
    Publication statusPublished - 2016

    Open Access - Access Right Statement

    © 2015 The Author(s). Published by Taylor & Francis. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.

    Keywords

    • Bayesian statistical decision theory
    • feedback
    • prediction
    • speech perception

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