• myGriffith
    • Staff portal
    • Contact Us⌄
      • Future student enquiries 1800 677 728
      • Current student enquiries 1800 154 055
      • International enquiries +61 7 3735 6425
      • General enquiries 07 3735 7111
      • Online enquiries
      • Staff phonebook
    View Item 
    •   Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    • Home
    • Griffith Research Online
    • Conference outputs
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

  • All of Griffith Research Online
    • Communities & Collections
    • Authors
    • By Issue Date
    • Titles
  • This Collection
    • Authors
    • By Issue Date
    • Titles
  • Statistics

  • Most Popular Items
  • Statistics by Country
  • Most Popular Authors
  • Support

  • Contact us
  • FAQs
  • Admin login

  • Login
  • Improvements to vowel categorization in non-native regional accents resulting from multiple- versus single-talker training: A computational approach

    Thumbnail
    View/Open
    WrightPUB1033.pdf (380.9Kb)
    File version
    Version of Record (VoR)
    Author(s)
    Wright, Sarah M.
    Shaw, Jason A.
    Best, Catherine T.
    Docherty, Gerry
    Evans, Bronwen G.
    Foulkes, Paul
    Hay, Jennifer
    Mulak, Karen
    Griffith University Author(s)
    Docherty, Gerry
    Year published
    2014
    Metadata
    Show full item record
    Abstract
    A computational modeling study was conducted using multinomial logistic regression to predict whether exposure to an unfamiliar regional accent of English would influence vowel categorization in (1) the exposure accent, (2) the native accent, and (3) another unfamiliar accent. We manipulated the number of talkers in the exposure data to determine whether talker variability influenced the efficacy of the training. Results showed a multiple-talker training benefit for the categorization of some vowels. Training also transferred to an untrained accent. Finally, the models predicted that exposure to an unfamiliar accent has a ...
    View more >
    A computational modeling study was conducted using multinomial logistic regression to predict whether exposure to an unfamiliar regional accent of English would influence vowel categorization in (1) the exposure accent, (2) the native accent, and (3) another unfamiliar accent. We manipulated the number of talkers in the exposure data to determine whether talker variability influenced the efficacy of the training. Results showed a multiple-talker training benefit for the categorization of some vowels. Training also transferred to an untrained accent. Finally, the models predicted that exposure to an unfamiliar accent has a negative impact on vowel categorization in the native accent.
    View less >
    Conference Title
    Proceedings of the 15th Australasian International Conference on Speech Science and Technology
    Publisher URI
    http://www.assta.org/?q=sst2014
    Copyright Statement
    © 2014 ASSTA. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the conference's website for access to the definitive, published version.
    Subject
    Laboratory Phonetics and Speech Science
    Publication URI
    http://hdl.handle.net/10072/123428
    Collection
    • Conference outputs

    Footer

    Disclaimer

    • Privacy policy
    • Copyright matters
    • CRICOS Provider - 00233E
    • TEQSA: PRV12076

    Tagline

    • Gold Coast
    • Logan
    • Brisbane - Queensland, Australia
    First Peoples of Australia
    • Aboriginal
    • Torres Strait Islander