• DocumentCode
    2793472
  • Title

    Discriminative HMMS, log-linear models, and CRFS: What is the difference?

  • Author

    Heigold, G. ; Wiesler, S. ; Nussbaum-Thom, Markus ; Lehnen, P. ; Schluter, R. ; Ney, H.

  • Author_Institution
    Comput. Sci. Dept., RWTH Aachen Univ., Aachen, Germany
  • fYear
    2010
  • fDate
    14-19 March 2010
  • Firstpage
    5546
  • Lastpage
    5549
  • Abstract
    Recently, there have been many papers studying discriminative acoustic modeling techniques like conditional random fields or discriminative training of conventional Gaussian HMMs. This paper will give an overview of the recent work and progress. We will strictly distinguish between the type of acoustic models on the one hand and the training criterion on the other hand. We will address two issues in more detail: the relation between conventional Gaussian HMMs and conditional random fields and the advantages of formulating the training criterion as a convex optimization problem. Experimental results for various speech tasks will be presented to carefully evaluate the different concepts and approaches, including both a digit string and large vocabulary continuous speech recognition tasks.
  • Keywords
    Gaussian processes; convex programming; hidden Markov models; speech recognition; Gaussian HMM; conditional random fields; convex optimization problem; digit string; discriminative acoustic modeling techniques; hidden Markov models; log-linear models; vocabulary continuous speech recognition tasks; Acoustic emission; Computer science; Constraint optimization; Hidden Markov models; Humans; Paper technology; Pattern recognition; Speech analysis; Speech recognition; Vocabulary; conditional random field; discriminative training; hidden Markov model; log-linear model; speech recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on
  • Conference_Location
    Dallas, TX
  • ISSN
    1520-6149
  • Print_ISBN
    978-1-4244-4295-9
  • Electronic_ISBN
    1520-6149
  • Type

    conf

  • DOI
    10.1109/ICASSP.2010.5495228
  • Filename
    5495228