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  • Spectral Subband Centroids for Robust Speaker Identification using Marginalization-based Missing Feature Theory

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    NicolsonPUB6170.pdf (532.9Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Nicolson, Aaron
    Hanson, Jack
    Lyons, James
    Paliwal, Kuldip
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Lyons, James
    Hanson, Jack S.
    Nicolson, Aaron M.
    Year published
    2018
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    Abstract
    Until now, marginalization-based Missing Feature Theory (MFT) for speech classification has been limited to the use of Log Spectral Subband Energies (LSSEs) as features. These features are highly correlated, thus suboptimal for classification with diagonal-covariance Gaussian Mixture Models (GMMs), a common classifier in marginalization-based MFT. In this paper, we propose that Spectral Subband Centroids (SSCs) are more apt for marginalization-based MFT, as they are both decorrelated and spectrally local. Our results show that SSCs as features produce a more robust marginalization-based MFT, diagonal-covariance GMM-based, ...
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    Until now, marginalization-based Missing Feature Theory (MFT) for speech classification has been limited to the use of Log Spectral Subband Energies (LSSEs) as features. These features are highly correlated, thus suboptimal for classification with diagonal-covariance Gaussian Mixture Models (GMMs), a common classifier in marginalization-based MFT. In this paper, we propose that Spectral Subband Centroids (SSCs) are more apt for marginalization-based MFT, as they are both decorrelated and spectrally local. Our results show that SSCs as features produce a more robust marginalization-based MFT, diagonal-covariance GMM-based, Automatic Speaker Identification (ASI) system than LSSEs as features, for at all tested SNR values (with Additive White Gaussian Noise (AWGN)). It is also shown that a fully-connected Deep Neural Network (DNN) can accurately estimate the Ideal Binary Mask (IBM) used for MFT.
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    Journal Title
    International Journal of Signal Processing Systems
    Volume
    6
    Issue
    1
    Publisher URI
    http://www.ijsps.com/index.php?m=content&c=index&a=show&catid=59&id=277
    Copyright Statement
    © 2018 Springer New York. This is an electronic version of an article published in Journal of Signal Processing Systems, Volume 6, No. 1, March 2018 > . Journal of Signal Processing Systems is available online at: http://link.springer.com/ with the open URL of your article.
    Subject
    Natural Language Processing
    Publication URI
    http://hdl.handle.net/10072/382262
    Collection
    • Journal articles

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