• DocumentCode
    2018037
  • Title

    SURE-MSE speech enhancement for robust speech recognition

  • Author

    Zheng, Nengheng ; Li, Xia ; Blu, Thierry ; Lee, Tan

  • Author_Institution
    Coll. of Inf. Eng., Shenzhen Univ., Shenzhen, China
  • fYear
    2010
  • fDate
    Nov. 29 2010-Dec. 3 2010
  • Firstpage
    271
  • Lastpage
    274
  • Abstract
    This paper presents a new approach to enhancing noisy (white Gaussian noise) speech signals for robust speech recognition. It is based on the minimization of an estimate of denoising MSE (known as SURE) and does not require any hypotheses on the original signal. The enhanced signal is obtained by thresholding coefficients in the DCT domain, with the parameters in the thresholding functions being specified through the minimization of the SURE. Thanks to a linear parametrization, this optimization is very cost-effective. This method also works well for non-white noise with a noise whitening processing before the optimization. We have performed automatic speech recognition tests on a subset of the AURORA 2 database, to compare our method with different denoising strategies. The results show that our method brings a substantial increase in recognition accuracy.
  • Keywords
    AWGN; discrete cosine transforms; mean square error methods; minimisation; signal denoising; speech enhancement; speech recognition; AURORA 2; DCT domain; SURE-MSE speech enhancement; Stein unbiased risk estimate; automatic speech recognition; denoising strategy; linear parametrization; noise minimization; noise whitening processing; robust speech recognition; speech thresholding coefficient; white Gaussian noise; Accuracy; Noise reduction; Signal to noise ratio; Speech; Speech enhancement; Speech recognition; MMSE; Speech enhancement; Stein´s unbiased risk estimate; automatic speech recognition;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Chinese Spoken Language Processing (ISCSLP), 2010 7th International Symposium on
  • Conference_Location
    Tainan
  • Print_ISBN
    978-1-4244-6244-5
  • Type

    conf

  • DOI
    10.1109/ISCSLP.2010.5684894
  • Filename
    5684894