Prosody and spoken-word recognition

This chapter outlines a Bayesian model of spoken-word recognition and reviews how prosody is part of that model. The review focuses on the information that assists the lis­ tener in recognizing the prosodic structure of an utterance and on how spoken-word recognition is also constrained by prior knowledge about prosodic structure. Recognition is argued to be a process of perceptual inference that ensures that listening is robust to variability in the speech signal. In essence, the listener makes inferences about the seg­ mental content of each utterance, about its prosodic structure (simultaneously at differ­ ent levels in the prosodic hierarchy), and about the words it contains, and uses these in­ ferences to form an utterance interpretation. Four characteristics of the proposed prosody-enriched recognition model are discussed: parallel uptake of different informa­ tion types, high contextual dependency, adaptive processing, and phonological abstrac­ tion. The next steps that should be taken to develop the model are also discussed