English  |  正體中文  |  简体中文  |  Items with full text/Total items : 25831/26425 (98%)
Visitors : 8146326      Online Users : 410
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version


    Please use this identifier to cite or link to this item: http://ir.lib.ksu.edu.tw/handle/987654321/17447


    Title: Exploiting principal component analysis in modulation spectrum enhancement for robust speech recognition
    Authors: 李詹儀
    Keywords: robust speech recognition;modulation spectrum;principal component analysis
    Date: 2011-07
    Issue Date: 2012-09-10 14:31:10 (UTC+8)
    Abstract: In this paper, we present a novel method to improve the noise robustness of speech features based on principal component analysis (PCA). The PCA process is employed to extract a set of basis spectral vectors for the modulation spectra of clean training speech features. The new modulation spectra of the speech features, constructed by mapping the original modulation spectra into the space spanned by these PCA-derived basis vectors, have shown robustness against the noise distortion. The experiments conducted on the Aurora-2 digit string database revealed that the proposed PCAbased approach, together with mean and variance normalization (MVN), can provide average error reduction rates of over 65% and 12% relative as compared with the baseline MFCC system and that using the MVN method alone, respectively.
    Appears in Collections:[環境工程系所] 期刊論文

    Files in This Item:

    File Description SizeFormat
    李詹儀-成果呈現.pdf336KbAdobe PDF586View/Open


    All items in KSUIR are protected by copyright, with all rights reserved.


    本網站之所有圖文內容授權為崑山科技大學圖書資訊館所有,請勿任意轉載或擷取使用。
    ©Kun Shan University Library and Information Center
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback