Title page for etd-0828112-195916


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URN etd-0828112-195916
Author Bing-Feng Yeh
Author's Email Address tjwyeh@hotmail.com
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Department Computer Science and Engineering
Year 2011
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Gaussian Mixture Model-based Feature Compensation with Application to Noise-robust Speech Recognition
Date of Defense 2012-07-31
Page Count 45
Keyword
  • MMSE
  • GMM
  • Noise Robustness
  • Abstract In this paper, we propose a new method for noise robustness base on Gaussian Mixture
    Model (GMM), and the method we proposed can estimate the noise feature effectively
    and reduce noise effect by plain fashion, and we can retain the smoothing and continuity
    from original feature in this way. Compared to the traditional feature transformation method
    MMSE(Minimum Mean Square Error) which want to find a clean one, the different is that
    the method we proposed only need to fine noise feature or the margin of noise effect and subtract
    the noise to achieve more robustness effect than traditional methods. In the experiment
    method, the test data pass through the trained noise classifier to judge the noise type and SNR,
    and according to the result of classifier to choose the corresponding transformation model and
    generate the noise feature by this model, and then we can use different weight linear combination
    to generate noise feature, and finally apply simple subtraction to achieve noise reduction.
    In the experiment, we use AURORA 2.0 corpus to estimate noise robustness performance,
    and using traditional method can achieve 36:8% relative improvement than default, and the
    our method can achieve 52:5% relative improvement, and compared to the traditional method
    our method can attain 24:9% relative improvement.
    Advisory Committee
  • Chung-Hsien Wu - chair
  • Wei-Bin Liang - co-chair
  • Chia-Ping Chen - advisor
  • Files
  • etd-0828112-195916.pdf
  • indicate access worldwide
    Date of Submission 2012-08-28

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