• DocumentCode
    1849273
  • Title

    Accent reduction for computer-aided language learning

  • Author

    Zhao, Sixuan ; Koh, Soo Ngee ; Luke, Kang Kwong

  • Author_Institution
    Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore
  • fYear
    2012
  • fDate
    27-31 Aug. 2012
  • Firstpage
    335
  • Lastpage
    339
  • Abstract
    This paper studies accent reduction techniques which are used to provide English learners with their own converted speech as a reference for speaking skills training. Three kinds of modifications, namely prosodic modification, segmental modification and combined modification are compared and examined using objective measurements. Two different corpora, a prosody abundant corpus and a prosody flat corpus, are used in our study. Modified utterances show a clear reduction of accentedness and an acceptable acoustic quality when compared with the original speech, demonstrating the effectiveness of the proposed accent reduction techniques. Furthermore, differences between experimental results from the two corpora in terms of the reduction of accentedness are observed and explanations for this phenomenon are presented. This paper also discusses other issues in this area for future research.
  • Keywords
    computer based training; natural language processing; speech processing; English learners; accent reduction techniques; accentedness reduction; acceptable acoustic quality; combined modification; computer-aided language learning; converted speech; language processing technologies; prosodic modification; prosody abundant corpus; prosody flat corpus; segmental modification; speaking skills training; speech processing technologies; Acoustics; Arctic; Educational institutions; Interpolation; Speech; Standards; Training; CALL; accent reduction; foreign accent;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European
  • Conference_Location
    Bucharest
  • ISSN
    2219-5491
  • Print_ISBN
    978-1-4673-1068-0
  • Type

    conf

  • Filename
    6333951