Feedforward and feedback in speech perception: Revisiting analysis by synthesis

We revisit the analysis by synthesis (A 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 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 wellaccounted for in the context of such an architecture. We focus on prediction in speech perception, an operation at the centre of the A 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 S should be reconsidered as a contender in speech recognition research, complementing currently more dominant models.