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Using acoustic distance to quantify lexical competition

  • Author(s) / Creator(s)
  • The present study quantifies the effects of lexical competition during spoken word recognition using acoustic distance, rather than phonological neighborhood density. The indication of a word's lexical competition is given by what is termed its acoustic distinctiveness, which is taken as its average acoustic distance to all other words in the lexicon. A variety of acoustic representations for items in the lexicon are analyzed. Statistical modeling shows that acoustic distinctiveness has a similar effect trend as phonological neigbhorhood density. Additionally, acoustic distinctiveness consistently increases model fitness more than phonological neighborhood density, regardless of which kind of acoustic representation is used. Acoustic distinctiveness does not seem to explain all the same things as phonological neighborhood density, however. The different areas that these two predictors explain are discussed, in addition to potential theoretical implications of acoustic distinctiveness's usefulness in models. The paper concludes with motiviations for why a researcher may want to use acoustic disinctiveness over phonological neighborhood density in future experiments. [This document was prepared as part of a generals paper course in the fall 2018 term.]

  • Date created
    2018-12-20
  • Subjects / Keywords
  • Type of Item
    Report
  • DOI
    https://doi.org/10.7939/r3-wbhs-kr84
  • License
    Attribution-NonCommercial 4.0 International