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

    Research output: Chapter in Book/Report/Conference proceedingConference 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 languageEnglish
    Title of host publication2016 23rd International Conference on Pattern Recognition, ICPR 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages2222-2227
    Number of pages6
    ISBN (Electronic)9781509048472
    DOIs
    Publication statusPublished - 2017 Apr 13
    Event23rd International Conference on Pattern Recognition, ICPR 2016 - Cancun, Mexico
    Duration: 2016 Dec 42016 Dec 8

    Other

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

    Fingerprint

    Uncertainty

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

    Cite this

    Ogawa, T., Mallidi, S. H., Dupoux, E., Cohen, J., Feldman, N. H., & Hermansky, H. (2017). 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] 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., 2017. p. 2222-2227 7899966.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Ogawa, T, Mallidi, SH, Dupoux, E, Cohen, J, Feldman, NH & Hermansky, H 2017, 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, 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. 2017. p. 2222-2227. 7899966 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., 2017. pp. 2222-2227
    @inproceedings{1fbb2cfe869e496aaa4ee7a23a654283,
    title = "A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation",
    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.",
    author = "Tetsuji Ogawa and Mallidi, {Sri Harish} and Emmanuel Dupoux and Jordan Cohen and Feldman, {Naomi H.} and Hynek Hermansky",
    year = "2017",
    month = "4",
    day = "13",
    doi = "10.1109/ICPR.2016.7899966",
    language = "English",
    pages = "2222--2227",
    booktitle = "2016 23rd International Conference on Pattern Recognition, ICPR 2016",
    publisher = "Institute of Electrical and Electronics Engineers Inc.",
    address = "United States",

    }

    TY - GEN

    T1 - A new efficient measure for accuracy prediction and its application to multistream-based unsupervised adaptation

    AU - Ogawa, Tetsuji

    AU - Mallidi, Sri Harish

    AU - Dupoux, Emmanuel

    AU - Cohen, Jordan

    AU - Feldman, Naomi H.

    AU - Hermansky, Hynek

    PY - 2017/4/13

    Y1 - 2017/4/13

    N2 - 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.

    AB - 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.

    UR - http://www.scopus.com/inward/record.url?scp=85019079432&partnerID=8YFLogxK

    UR - http://www.scopus.com/inward/citedby.url?scp=85019079432&partnerID=8YFLogxK

    U2 - 10.1109/ICPR.2016.7899966

    DO - 10.1109/ICPR.2016.7899966

    M3 - Conference contribution

    AN - SCOPUS:85019079432

    SP - 2222

    EP - 2227

    BT - 2016 23rd International Conference on Pattern Recognition, ICPR 2016

    PB - Institute of Electrical and Electronics Engineers Inc.

    ER -