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  • Noise adaptive speech recognition in time-varying noise based on sequential Kullback proximal algorithm

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    Author(s)
    Yao, KS
    Paliwal, KK
    Nakamura, S
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2002
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    Abstract
    We present a noise adaptive speech recognition approach, where time-varying noise parameter estimation and Viterbi process are combined together. The Viterbi process provides approximated joint likelihood of active partial paths and observation sequence given the noise parameter sequence estimated till previous frame. The joint likelihood after normalization provides approximation to the posterior probabilities of state sequences for an EM-type recursive process based on the sequential Kullback proximal algorithm to estimate the current noise parameter. The combined process can easily be applied to perform continuous speech ...
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    We present a noise adaptive speech recognition approach, where time-varying noise parameter estimation and Viterbi process are combined together. The Viterbi process provides approximated joint likelihood of active partial paths and observation sequence given the noise parameter sequence estimated till previous frame. The joint likelihood after normalization provides approximation to the posterior probabilities of state sequences for an EM-type recursive process based on the sequential Kullback proximal algorithm to estimate the current noise parameter. The combined process can easily be applied to perform continuous speech recognition in presence of non-stationary noise. Experiments were conducted in simulated and real non-stationary noises. Results showed that the noise adaptive system provides significant improvements in word accuracy as compared to the baseline system (without noise compensation) and the normal noise compensation system (which assumes the noise to be stationary).
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    Conference Title
    2002 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING, VOLS I-IV, PROCEEDINGS
    Volume
    1
    Publisher URI
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7874
    DOI
    https://doi.org/10.1109/ICASSP.2002.5743686
    Copyright Statement
    © 2002 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/10072/9087
    Collection
    • Conference outputs

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