A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation

Tetsuji Ogawa, Sri Harish Mallidi, Emmanuel Dupoux, Jordan Cohen, Naomi H. Feldman, Hynek Hermansky

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

A new efficient measure for predicting estimation accuracy is proposed and successfully applied to multistream-based unsupervised adaptation of ASR systems to address data uncertainty when the ground-truth is unknown. The proposed measure is an extension of the M-measure, which predicts confidence in the output of a probability estimator by measuring the divergences of probability estimates spaced at specific time intervals. In this study, the M-measure was extended by considering the latent phoneme information, resulting in an improved reliability. Experimental comparisons carried out in a multistream-based ASR paradigm demonstrated that the extended M-measure yields a significant improvement over the original M-measure, especially under narrow-band noise conditions.

本文言語English
ホスト出版物のタイトル2016 23rd International Conference on Pattern Recognition, ICPR 2016
出版社Institute of Electrical and Electronics Engineers Inc.
ページ2222-2227
ページ数6
ISBN(電子版)9781509048472
DOI
出版ステータスPublished - 2016 1 1
イベント23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
継続期間: 2016 12 42016 12 8

出版物シリーズ

名前Proceedings - International Conference on Pattern Recognition
0
ISSN(印刷版)1051-4651

Other

Other23rd International Conference on Pattern Recognition, ICPR 2016
CountryMexico
CityCancun
Period16/12/416/12/8

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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