Uncertainty estimation of DNN classifiers

Sri Harish Mallidi, Tetsuji Ogawa, Hynek Hermansky

研究成果: Conference contribution

22 被引用数 (Scopus)

抄録

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.

本文言語English
ホスト出版物のタイトル2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ283-288
ページ数6
ISBN(電子版)9781479972913
DOI
出版ステータス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

出版物シリーズ

名前2015 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015 - Proceedings

Other

OtherIEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2015
国/地域United States
CityScottsdale
Period15/12/1315/12/17

ASJC Scopus subject areas

  • 人工知能
  • コンピュータ ネットワークおよび通信
  • コンピュータ ビジョンおよびパターン認識

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