• DocumentCode
    2874149
  • Title

    The Research of Associational lip-reading Recognition Based on Bayesian Theory

  • Author

    Yong-Hui, Huang ; Bao-chang, Pan ; Sheng-Lin, Zheng

  • Author_Institution
    Fac. of Autom., Guangdong Univ. of Technol., Guangzhou
  • fYear
    2007
  • fDate
    16-18 April 2007
  • Firstpage
    134
  • Lastpage
    137
  • Abstract
    The key of lip-reading recognition is to analyze rules of lip shapes, and build up the corresponding relationships between lip shapes and Chinese speeches, but the difficulty of the research lies in the "one-to-many" relationship between lip shape and speeches. For this problem, the associational lip-reading recognition based on Bayesian theory was put forward by this paper. The method applied Bayesian theory to recognize Chinese speeches, simultaneously, dynamically and self-adaptively adjusted the probabilities of all speeches corresponded by the same lip shape based on an award and penalty strategy, consequently tried to reflect speaker\´s real intention to the greatest extent. The results verified that this method could recognize effectively Chinese characters having the same lip shape, and enhanced the lip-reading system\´s practicability and validity.
  • Keywords
    Bayes methods; computer vision; feature extraction; natural languages; object recognition; probability; Bayesian theory; Chinese speech; associational lip-reading recognition; lip shapes; one-to-many relationship; probability; Automatic speech recognition; Automation; Bayesian methods; Character recognition; Information analysis; National security; Shape; Speech analysis; Speech processing; Speech recognition; Bayesian theory; associational style; lip-reading recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Anti-counterfeiting, Security, Identification, 2007 IEEE International Workshop on
  • Conference_Location
    Xiamen, Fujian
  • Print_ISBN
    1-4244-1035-5
  • Electronic_ISBN
    1-4244-1035-5
  • Type

    conf

  • DOI
    10.1109/IWASID.2007.373713
  • Filename
    4244799