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Ambiguity, Competition, and Blending in Spoken Word Recognition
Author(s) -
Gaskell M. Gareth,
Marslen–Wilson William D.
Publication year - 1999
Publication title -
cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.498
H-Index - 114
eISSN - 1551-6709
pISSN - 0364-0213
DOI - 10.1207/s15516709cog2304_3
Subject(s) - ambiguity , coactivation , mental lexicon , lexicon , perception , computer science , speech recognition , speech perception , property (philosophy) , curse of dimensionality , word (group theory) , set (abstract data type) , natural language processing , artificial intelligence , psychology , linguistics , philosophy , epistemology , neuroscience , psychiatry , electromyography , programming language
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 can 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 (Gaskell & Marslen–Wilson, 1999).