Title page for etd-0207113-161555


[Back to Results | New Search]

URN etd-0207113-161555
Author Yo-Ping Wang
Author's Email Address No Public.
Statistics This thesis had been viewed 5537 times. Download 1633 times.
Department Computer Science and Engineering
Year 2012
Semester 1
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Gaussian Mixture Model with Application to Automatic Speech Recognition Feature Compensation in the Evaluation of Noisy Digital Corpora of Four Languages
Date of Defense 2013-01-30
Page Count 48
Keyword
  • GMM
  • Noise Robustness
  • MMSE
  • AURORA 3.0
  • Abstract   According to the traditional methods of noise robustness, the Minimum Mean Square Error(MMSE) feature transformation method was usually used to estimate clean feature. In order to maintain the smoothness and continuity from original feature, we use the method of noise robustness which is based on Gaussian Mixture Model to remove the noise instead of estimating the clean feature. Our method assumed that the lower noisy corpus of parallel corpora is the clean one. We find the mean vector corresponding to the noise by using the trained Gaussian Mixture Model, and use the concept of MMSE to calculate the margin of a noise effect. Finally we estimated the distance between noise feature of parallel corpura by MMSE and subtracted it from the higher noise feature. We use AURURA 3.0 corpus by experiment to estimate noise robustness performance. Test data will be classified by the trained noise classfier, and select the corresponding GMM mapping model, estimated the mean noise vectors under this model and generated the noise feature through a linear combination of unequal weight. Finally, it is easy to remove the noise by subtraction to make noise reduction.
    Advisory Committee
  • Tsung-hsien Wu - chair
  • Wei-Bin Liang - co-chair
  • Chia-Ping Chen - advisor
  • Files
  • etd-0207113-161555.pdf
  • indicate access worldwide
    Date of Submission 2013-02-07

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have more questions or technical problems, please contact eThesys