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  • MMSE estimation of log-filter bank energies for robust speech recognition

    Author(s)
    Stark, A
    Paliwal, K
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2011
    Metadata
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    Abstract
    In this paper, we derive a minimum mean square error log-filterbank energy estimator for environment-robust automatic speech recognition. While several such estimators exist within the literature, most involve trade-offs between simplifications of the log-filterbank noise distortion model and analytical tractability. To avoid this limitation, we extend a well known spectral domain noise distortion model for use in the log-filterbank energy domain. To do this, several mathematical transformations are developed to transform spectral domain models into filterbank and log-filterbank energy models. As a result, a new estimator ...
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    In this paper, we derive a minimum mean square error log-filterbank energy estimator for environment-robust automatic speech recognition. While several such estimators exist within the literature, most involve trade-offs between simplifications of the log-filterbank noise distortion model and analytical tractability. To avoid this limitation, we extend a well known spectral domain noise distortion model for use in the log-filterbank energy domain. To do this, several mathematical transformations are developed to transform spectral domain models into filterbank and log-filterbank energy models. As a result, a new estimator is developed that allows for robust estimation of both log-filterbank energies and subsequent Mel-frequency cepstral coefficients. The proposed estimator is evaluated over the Aurora2, and RM speech recognition tasks, with results showing a significant reduction in word recognition error over both baseline results and several competing estimators.
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    Journal Title
    Speech Communication
    Volume
    53
    Issue
    3
    DOI
    https://doi.org/10.1016/j.specom.2010.11.004
    Subject
    Artificial intelligence not elsewhere classified
    Cognitive and computational psychology
    Linguistics
    Publication URI
    http://hdl.handle.net/10072/44400
    Collection
    • Journal articles

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