• DocumentCode
    661304
  • Title

    Predicting gradation of L2 English mispronunciations using ASR with extended recognition network

  • Author

    Hao Wang ; Meng, Hsiang-Yun ; Xiaojun Qian

  • Author_Institution
    Dept. of Syst. Eng. & Eng. Manage., Chinese Univ. of Hong Kong, Hong Kong, China
  • fYear
    2013
  • fDate
    Oct. 29 2013-Nov. 1 2013
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    A CAPT system can be pedagogically improved by giving effective feedback according to the severity of mispronunciations. We obtained perceptual gradations of L2 English mispronunciations through crowdsourcing, conducted quality control to filter for reliable ratings and proposed approaches to predict gradation of word-level mispronunciations. This paper presents our work on making improvements using ASR with extended recognition network to the previous predicting approach to solve its limitations: 1. it is not working for those mispronounced words whose transcriptions are not immediately available; 2. perceptually differently articulated words with the same transcription have the same predicted gradation.
  • Keywords
    computer based training; natural language processing; quality control; speech recognition; ASR; CAPT system; L2 English mispronunciations; computer-assisted pronunciation training; crowdsourcing; extended recognition network; gradation prediction; quality control; word-level mispronunciations; Linear regression; Manuals; Reliability; Speech; Speech recognition; Training; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific
  • Conference_Location
    Kaohsiung
  • Type

    conf

  • DOI
    10.1109/APSIPA.2013.6694165
  • Filename
    6694165