Enhancing Eigenspace-Based MLLR Speaker Adaptation Using a Fuzzy Logic Learning Control Scheme

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著者

    • DING Ing-Jr
    • the Department of Electrical Engineering, National Formosa University

抄録

This study develops a fuzzy logic control mechanism in eigenspace-based MLLR speaker adaptation. Specifically, this mechanism can determine hidden Markov model parameters to enhance overall recognition performance despite ordinary or adverse conditions in both training and operating stages. The proposed mechanism regulates the influence of eigenspace-based MLLR adaptation given insufficient training data from a new speaker. This mechanism accounts for the amount of adaptation data available in transformation matrix parameter smoothing, and thus ensures the robustness of eigenspace-based MLLR adaptation against data scarcity. The proposed adaptive learning mechanism is computationally inexpensive. Experimental results show that eigenspace-based MLLR adaptation with fuzzy control outperforms conventional eigenspace-based MLLR, and especially when the adaptation data acquired from a new speaker is insufficient.

収録刊行物

  • IEICE transactions on information and systems

    IEICE transactions on information and systems 94(10), 1909-1916, 2011-10-01

    一般社団法人 電子情報通信学会

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各種コード

  • NII論文ID(NAID)
    10030193256
  • NII書誌ID(NCID)
    AA10826272
  • 本文言語コード
    ENG
  • 資料種別
    ART
  • ISSN
    09168532
  • データ提供元
    CJP書誌  J-STAGE 
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