Ambiguity, competition, and blending in spoken word recognition

A critical property of the perception of spoken words is the transient ambiguity of the speech signal. In localist models of speech perception this ambiguity is captured by allowing the parallel activation of multiple lexical representations. This paper examines how a distributed model of speech perception con accommodate this property. Statistical analyses of vector spaces show that coactivation of multiple distributed representations is inherently noisy, and depends on parameters such as sparseness and dimensionality. Furthermore, the characteristics of coactivation vary considerably, depending on the organization of distributed representations within the mental lexicon. This view of lexical access is supported by analyses of phonological and semantic word representations, which provide an explanation of a recent set of experiments on coactivation in speech perception.