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date: 09 December 2019

Abstract and Keywords

Spoken word recognition is the study of how lexical representations are accessed from phonological patterns in the speech signal. That is, we conventionally make two simplifying assumptions: Because many fundamental problems in speech perception remain unsolved, we provisionally assume the input is a string of phonemes that are the output of speech perception processes, and that the output is a string of recognized words that are passed onto mechanisms supporting, for example, sentence processing. These kinds of assumptions allow psycholinguists to break language understanding into manageable research domains, and as we review, they have afforded great progress in understanding spoken word recognition. However, we also review a growing body of results that are incompatible with these assumptions: Spoken word recognition is constrained by subphonemic details and top-down influences from higher level processing. We argue that these findings are incompatible with current theoretical frameworks, and that a new theoretical paradigm is needed.

Keywords: lexical access, computational models, segmentation, embedded word problem, interaction, top-down influences

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