• DocumentCode
    1101652
  • Title

    A decision theorectic formulation of a training problem in speech recognition and a comparison of training by unconditional versus conditional maximum likelihood

  • Author

    Nádas, Arthur

  • Author_Institution
    IBM T.J. Watson Research Center, Yorktown Heights, NY
  • Volume
    31
  • Issue
    4
  • fYear
    1983
  • fDate
    8/1/1983 12:00:00 AM
  • Firstpage
    814
  • Lastpage
    817
  • Abstract
    The choice of method for training a speech recognizer is posed as an optimization problem. The currently used method of maximum likelihood, while heuristic, is shown to be superior under certain assumptions to another heuristic: the method of conditional maximum likelihood.
  • Keywords
    Automatic speech recognition; Feature extraction; Maximum likelihood decoding; Microphones; Optimization methods; Predictive models; Signal processing; Speech recognition; Statistical analysis; Vectors;
  • fLanguage
    English
  • Journal_Title
    Acoustics, Speech and Signal Processing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0096-3518
  • Type

    jour

  • DOI
    10.1109/TASSP.1983.1164173
  • Filename
    1164173
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1101652