On building models of spoken-word recognition: when there is as much to learn from natural "oddities" as artificial normality.

Much of what we know about spoken-word recognition comes from studies relying on speech stimuli either carefully produced in the laboratory or computer altered. Although such stimuli have allowed key constructs to be highlighted, the extent to which these constructs are operative in the processing of everyday speech is unclear. We argue that studying the recognition of naturally occurring degraded speech, such as that produced by individuals with neurological disease, can improve the external validity of existing spoken-word recognition models. This claim is illustrated in an experiment on the effect of talker-specific (indexical) variations on lexical access. We found that talker specificity effects, wherein words are better recalled if played in the same voice than in a different voice between two consecutive blocks, were greater when the words were spoken by dysarthric than by healthy individuals. The effects were found to relate to the increased processing time caused by the dysarthric stimuli, independently of their reduced intelligibility. This result is consistent with Luce, McLennan, and Charles-Luce's (2003) time-course hypothesis, which posits that reliance on indexical details increases when responses are delayed by suboptimal processing conditions. We conclude by advocating the use of laboratory and naturally occurring degraded speech in tandem and more systematic cross-talks between psycholinguistics and the speech sciences.