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

doi:10.1109/ICPR.2016.7899966

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

School of Fundamental Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

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.

Original language	English
Title of host publication	2016 23rd International Conference on Pattern Recognition, ICPR 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2222-2227
Number of pages	6
ISBN (Electronic)	9781509048472
DOIs	https://doi.org/10.1109/ICPR.2016.7899966
Publication status	Published - 2016 Jan 1
Event	23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico Duration: 2016 Dec 4 → 2016 Dec 8

Publication series

Name	Proceedings - International Conference on Pattern Recognition
Volume	0
ISSN (Print)	1051-4651

Other

Other	23rd International Conference on Pattern Recognition, ICPR 2016
Country	Mexico
City	Cancun
Period	16/12/4 → 16/12/8

ASJC Scopus subject areas

Computer Vision and Pattern Recognition

Access to Document

10.1109/ICPR.2016.7899966

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Cite this

Ogawa, T., Mallidi, S. H., Dupoux, E., Cohen, J., Feldman, N. H., & Hermansky, H. (2016). A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016 (pp. 2222-2227). [7899966] (Proceedings - International Conference on Pattern Recognition; Vol. 0). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICPR.2016.7899966

A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation. / Ogawa, Tetsuji; Mallidi, Sri Harish; Dupoux, Emmanuel; Cohen, Jordan; Feldman, Naomi H.; Hermansky, Hynek.

2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2222-2227 7899966 (Proceedings - International Conference on Pattern Recognition; Vol. 0).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Ogawa, T, Mallidi, SH, Dupoux, E, Cohen, J, Feldman, NH & Hermansky, H 2016, A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation. in 2016 23rd International Conference on Pattern Recognition, ICPR 2016., 7899966, Proceedings - International Conference on Pattern Recognition, vol. 0, Institute of Electrical and Electronics Engineers Inc., pp. 2222-2227, 23rd International Conference on Pattern Recognition, ICPR 2016, Cancun, Mexico, 16/12/4. https://doi.org/10.1109/ICPR.2016.7899966

Ogawa T, Mallidi SH, Dupoux E, Cohen J, Feldman NH, Hermansky H. A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation. In 2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2222-2227. 7899966. (Proceedings - International Conference on Pattern Recognition). https://doi.org/10.1109/ICPR.2016.7899966

Ogawa, Tetsuji ; Mallidi, Sri Harish ; Dupoux, Emmanuel ; Cohen, Jordan ; Feldman, Naomi H. ; Hermansky, Hynek. / A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation. 2016 23rd International Conference on Pattern Recognition, ICPR 2016. Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2222-2227 (Proceedings - International Conference on Pattern Recognition).

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