Entry Bang:2014:PVP from talip.bib

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BibTeX entry

@Article{Bang:2014:PVP,
  author =       "Jeesoo Bang and Jonghoon Lee and Gary Geunbae Lee and
                 Minhwa Chung",
  title =        "Pronunciation Variants Prediction Method to Detect
                 Mispronunciations by {Korean} Learners of {English}",
  journal =      j-TALIP,
  volume =       "13",
  number =       "4",
  pages =        "16:1--16:??",
  month =        dec,
  year =         "2014",
  CODEN =        "????",
  DOI =          "https://doi.org/10.1145/2629545",
  ISSN =         "1530-0226 (print), 1558-3430 (electronic)",
  ISSN-L =       "1530-0226",
  bibdate =      "Wed Jan 7 15:23:49 MST 2015",
  bibsource =    "http://portal.acm.org/;
                 http://www.math.utah.edu/pub/tex/bib/talip.bib",
  abstract =     "This article presents an approach to nonnative
                 pronunciation variants modeling and prediction. The
                 pronunciation variants prediction method was developed
                 by generalized transformation-based error-driven
                 learning (GTBL). The modified goodness of pronunciation
                 (GOP) score was applied to effective mispronunciation
                 detection using logistic regression machine learning
                 under the pronunciation variants prediction.
                 English-read speech data uttered by Korean-speaking
                 learners of English were collected, then pronunciation
                 variation knowledge was extracted from the differences
                 between the canonical phonemes and the actual phonemes
                 of the speech data. With this knowledge, an
                 error-driven learning approach was designed that
                 automatically learns phoneme variation rules from
                 phoneme-level transcriptions. The learned rules
                 generate an extended recognition network to detect
                 mispronunciations. Three different mispronunciation
                 detection methods were tested including our logistic
                 regression machine learning method with modified GOP
                 scores and mispronunciation preference features; all
                 three methods yielded significant improvement in
                 predictions of pronunciation variants, and our logistic
                 regression method showed the best performance.",
  acknowledgement = ack-nhfb,
  articleno =    "16",
  fjournal =     "ACM Transactions on Asian Language Information
                 Processing",
  journal-URL =  "http://portal.acm.org/browse_dl.cfm?idx=J820",
}

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