• DocumentCode
    3431760
  • Title

    Towards machines that know when they do not know: Summary of work done at 2014 Frederick Jelinek Memorial Workshop

  • Author

    Hermansky, Hynek ; Burget, Lukas ; Cohen, Jordan ; Dupoux, Emmanuel ; Feldman, Naomi ; Godfrey, John ; Khudanpur, Sanjeev ; Maciejewski, Matthew ; Mallidi, Sri Harish ; Menon, Anjali ; Ogawa, Tetsuji ; Peddinti, Vijayaditya ; Rose, Richard ; Stern, Richar

  • Author_Institution
    Johns Hopkins Univ., Baltimore, MD, USA
  • fYear
    2015
  • fDate
    19-24 April 2015
  • Firstpage
    5009
  • Lastpage
    5013
  • Abstract
    A group of junior and senior researchers gathered as a part of the 2014 Frederick Jelinek Memorial Workshop in Prague to address the problem of predicting the accuracy of a nonlinear Deep Neural Network probability estimator for unknown data in a different application domain from the domain in which the estimator was trained. The paper describes the problem and summarizes approaches that were taken by the group1.
  • Keywords
    neural nets; probability; speech recognition; 2014 Frederick Jelinek Memorial Workshop; nonlinear deep neural network probability estimator; speech recognition; Accuracy; Correlation; Estimation; Noise; Speech; Speech recognition; Training data; Performance monitoring; confidence estimation; multistream recognition of speech;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on
  • Conference_Location
    South Brisbane, QLD
  • Type

    conf

  • DOI
    10.1109/ICASSP.2015.7178924
  • Filename
    7178924
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3431760