Uncertainty estimation of DNN classifiers

Sri Harish Mallidi, Tetsuji Ogawa, Hynek Hermansky

    研究成果: Conference contribution

    19 引用 (Scopus)

    抜粋

    New efficient measures for estimating uncertainty of deep neural network (DNN) classifiers are proposed and successfully applied to multistream-based unsupervised adaptation of ASR systems to address uncertainty derived from noise. The proposed measure is the error from associative memory models trained on outputs of a DNN. In the present study, an attempt is made to use autoencoders for remembering the property of data. Another measure proposed is an extension of the M-measure, which computes the divergences of probability estimates spaced at specific time intervals. The extended measure results in an improved reliability by considering the latent information of phoneme duration. Experimental comparisons carried out in a multistream-based ASR paradigm demonstrates that the proposed measures yielded improvements over the multistyle trained system and system selected based on existing measures. Fusion of the proposed measures achieved almost the same performance as the oracle system selection.

    元の言語English
    ホスト出版物のタイトル2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Proceedings
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ283-288
    ページ数6
    ISBN(印刷物)9781479972913
    DOI
    出版物ステータスPublished - 2016 2 10
    イベントIEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Scottsdale, United States
    継続期間: 2015 12 132015 12 17

    Other

    OtherIEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015
    United States
    Scottsdale
    期間15/12/1315/12/17

      フィンガープリント

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Networks and Communications
    • Computer Vision and Pattern Recognition

    これを引用

    Mallidi, S. H., Ogawa, T., & Hermansky, H. (2016). Uncertainty estimation of DNN classifiers. : 2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Proceedings (pp. 283-288). [7404806] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASRU.2015.7404806