• DocumentCode
    3544712
  • Title

    Robust Automatic Speech Recognition System: Hmm Versus Sparse

  • Author

    Mohammed, Musfir ; Edet, B.K. ; Carrol, X.C. ; Yasif, K.A. ; Rahamathulla ; Supriya, V.

  • Author_Institution
    Dept. of Electron. & Commun. Eng., MES Coll. of Eng., Kuttippuram, India
  • fYear
    2012
  • fDate
    8-10 Feb. 2012
  • Firstpage
    339
  • Lastpage
    342
  • Abstract
    Speech Recognition has been an ever growing and challenging area for the researchers as well as the industry. It is defined in the computer domain as the ability to ability of computer systems to accept spoken words in audio format - such as. wav or raw and perform tasks accordingly. Despite the wide diffusion of commercial applications most of the research works are done in either English, Arabic or Mandarin and the technology underlying is known to only a few laboratories[1]. Thus the development of such a system is still on the primitive stage towards the local Indian languages. This paper discusses regarding an attempt to develop a digit recognition system for Malayalam language using the HMM Toolkit (HTK). Application of Sparse Imputation to recognition algorithm is discussed so as to increase the robustness rather than the conventional Hidden Markov Model technique.
  • Keywords
    hidden Markov models; speech recognition; Arabic language; English language; HMM toolkit; Indian language; Mandarin language; automatic speech recognition system; hidden Markov model; sparse imputation; spoken words; Hidden Markov models; Mel frequency cepstral coefficient; Speech; Speech recognition; Training; Vectors; Vocabulary; Automatic Speech Recognition; BFCC; HMM; MFCC; RPLP; Sparse;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Systems, Modelling and Simulation (ISMS), 2012 Third International Conference on
  • Conference_Location
    Kota Kinabalu
  • Print_ISBN
    978-1-4673-0886-1
  • Type

    conf

  • DOI
    10.1109/ISMS.2012.66
  • Filename
    6169725