• DocumentCode
    1289005
  • Title

    Missing-Feature Reconstruction With a Bounded Nonlinear State-Space Model

  • Author

    Remes, Ulpu ; Palomäki, Kalle J. ; Raiko, Tapani ; Honkela, Antti ; Kurimo, Mikko

  • Author_Institution
    Sch. of Sci., Adaptive Inf. Res. Centre, Aalto Univ., Espoo, Finland
  • Volume
    18
  • Issue
    10
  • fYear
    2011
  • Firstpage
    563
  • Lastpage
    566
  • Abstract
    Missing-feature reconstruction can improve speech recognition performance in unknown noisy environments. In this work, we examine using a nonlinear state-space model (NSSM) for missing-feature reconstruction and propose estimation with observed bounds to improve the NSSM performance. Evaluated in large-vocabulary continuous speech recognition task with babble and impulsive noise, using observed bounds in NSSM state estimation significantly improved the method performance.
  • Keywords
    signal reconstruction; speech recognition; state estimation; NSSM state estimation; babble noise; bounded nonlinear state-space model; impulsive noise; large-vocabulary continuous speech recognition task; missing-feature reconstruction; speech recognition performance; Hidden Markov models; Mathematical model; Noise measurement; Signal to noise ratio; Speech; Speech recognition; Missing data; noise robustness; speech recognition; state space methods;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2011.2163508
  • Filename
    5971765
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1289005