期刊
LANGUAGE AND COGNITIVE PROCESSES
卷 26, 期 7, 页码 935-951出版社
ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD
DOI: 10.1080/01690965.2010.493301
关键词
Perception; Bayesian; Cognitive neuroscience
We revisit the analysis by synthesis (A x S) approach to speech recognition. In the late 1950s and 1960s, Stevens and Halle proposed a model of spoken word recognition in which candidate word representations were synthesised from brief cues in the auditory signal and analysed against the input signal in tightly linked bottom-up/top-down fashion. While this approach failed to garner much support at the time, recent years have brought a surge of interest in Bayesian approaches to perception, and the idea of A x S has consequently gained attention, particularly in the domain of visual perception. We review the model and illustrate some data from speech perception that are well-accounted for in the context of such an architecture. We focus on prediction in speech perception, an operation at the centre of the A x S algorithm. The data reviewed here and the current possibilities to study online measures of speech processing using cognitive neuroscience methods, in our view, add to a provocative series of arguments why A x S should be reconsidered as a contender in speech recognition research, complementing currently more dominant models.
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