• DocumentCode
    2006346
  • Title

    Bimodal Speech Recognition Based on Hierarchical Parallel Boosting

  • Author

    Qin Wei ; Wei Gang ; Yu Wei-Yu

  • Author_Institution
    South China Univ. of Technol., Guangzhou
  • fYear
    2007
  • fDate
    May 30 2007-June 1 2007
  • Firstpage
    1752
  • Lastpage
    1755
  • Abstract
    In this paper, a weak learning algorithm is boosted to a strong effective learning algorithm using the boosting algorithm. The traditional boosting algorithm costs a great deal of running time. In order to decrease the running time, a hierarchical parallel boosting algorithm is proposed, which supports the multi-class classifying problem. Some simple classifiers using this algorithm can be trained in parallel. In addition, within one classifier, models for every class can be trained in parallel, too. Experimental results of bimodal speech recognition show that the new algorithm is able to produce classifiers as accurate as the traditional boosting classifier with the same number of base classifiers, but with greatly reduced running time.
  • Keywords
    hidden Markov models; learning (artificial intelligence); signal classification; speech recognition; bimodal speech recognition; hidden Markov model; hierarchical parallel boosting; multiclass classifying problem; weak learning algorithm; Acoustic noise; Acoustical engineering; Automatic control; Automation; Boosting; Costs; Hidden Markov models; Loudspeakers; Speech recognition; Voting; Bimodal Speech Recognition; Hidden Markov Model; Parallel Boosting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Automation, 2007. ICCA 2007. IEEE International Conference on
  • Conference_Location
    Guangzhou
  • Print_ISBN
    978-1-4244-0817-7
  • Type

    conf

  • DOI
    10.1109/ICCA.2007.4376661
  • Filename
    4376661