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  • Multi-frame GMM-based block quantisation for distributed speech recognition under noisy conditions

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    Author(s)
    So, Stephen
    Paliwal, Kuldip K
    Griffith University Author(s)
    Paliwal, Kuldip K.
    So, Stephen
    Year published
    2006
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    Abstract
    In this paper, we report on the recognition accuracy of the multiframe GMM-based block quantiser for the coding of MFCC features in a distributed speech recognition framework under varying noise conditions. All experiments were performed using the ETSI Aurora-2 connected-digits recognition task. For comparison, we have also investigated other quantisation schemes such as the memoryless GMM-based block quantiser, the unconstrained vector quantiser, and non-uniform scalar quantisers. The results show that the rate-distortion efficiency of the quantiser is a factor in determining the level of recognition accuracy at low to ...
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    In this paper, we report on the recognition accuracy of the multiframe GMM-based block quantiser for the coding of MFCC features in a distributed speech recognition framework under varying noise conditions. All experiments were performed using the ETSI Aurora-2 connected-digits recognition task. For comparison, we have also investigated other quantisation schemes such as the memoryless GMM-based block quantiser, the unconstrained vector quantiser, and non-uniform scalar quantisers. The results show that the rate-distortion efficiency of the quantiser is a factor in determining the level of recognition accuracy at low to medium levels of additive noise. For high levels of additive noise, the influence of rate-distortion efficiency diminishes and the recognition accuracy becomes dependent on the recognition features.
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    Conference Title
    2006 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-13
    Volume
    1
    Publisher URI
    http://ieeexplore.ieee.org/servlet/opac?punumber=11024
    DOI
    https://doi.org/10.1109/ICASSP.2006.1659989
    Copyright Statement
    © 2006 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
    Publication URI
    http://hdl.handle.net/10072/12322
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    • Conference outputs

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