Merging information in speech recognition: Feedback is never necessary

By Dennis Norris, James M. McQueen, Anne Cutler. Behavioral and Brain Sciences, 2000

731 citations recorded by the source corpus.

Subfields: Cultural Studies, Experimental and Cognitive Psychology, Language and Linguistics

View Paper: https://www.semanticscholar.org/paper/096f739e97b99a08354ca3860fa55ad8a4aefaee

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Citations in the corpus

The spatial coding model of visual word identification. (2010, background): “This is an area for future investigation, and the study of subset priming may be fertile territory for the continuing debate between interactive and noninteractive models of perception (e.g., Bowers & Davis, 2004; McClelland, Mirman, & Holt, 2006; Norris et al., 2000).”
Effects of orthography on speech production in a form-preparation paradigm (2003, background): “And again, various evidence suggests this is the case (e.g., Samuel, 1997), although there are modular interpretations of such findings as well (e.g., Norris, McQueen, & Cutler, 2000 ).”
Neighborhood effects in auditory word recognition: Phonological competition and orthographic facilitation. (2003, background): “In fact, many spoken word recognition models, such as TRACE (McClelland & Elman, 1986), SHORTLIST (Norris, 1994), MERGE ( Norris, McQueen, & Cutler, 2000 ) or NAM (Luce & Pisoni, 1998), propose that word representations of similar sounding words, so-called lexical neighbors, are activated during wor”
Lexical activation (and other factors) can mediate compensation for coarticulation (2003, background): “Given that compensation is clearly prelexical (a view strongly endorsed by Norris et al., 2000), these results appear to offer clear evidence for lexical activation having a top–down effect on phonetic perception.”
Learning pitch patterns in lexical identification by native English-speaking adults (2007, result): “If they indeed possess increased ability in lexical learning, that would suggest a more basic and general auditory mechanism contributes to spoken language learning, which is consistent with the general framework of bottom-up models (e.g., Norris et al., 2000 ) and models suggesting detailed acoust”