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  • Feature extraction from higher-lag autocorrelation coefficients for robust speech recognition

    Author(s)
    Shannon, Benjamin J
    Paliwal, Kuldip K
    Griffith University Author(s)
    Paliwal, Kuldip K.
    Year published
    2006
    Metadata
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    Abstract
    In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower-time lags, while the higher-lag autocorrelation coefficients are least affected, this method discards the lower-lag autocorrelation coefficients and uses only the higher-lag autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-lag autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power ...
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    In this paper, a feature extraction method that is robust to additive background noise is proposed for automatic speech recognition. Since the background noise corrupts the autocorrelation coefficients of the speech signal mostly at the lower-time lags, while the higher-lag autocorrelation coefficients are least affected, this method discards the lower-lag autocorrelation coefficients and uses only the higher-lag autocorrelation coefficients for spectral estimation. The magnitude spectrum of the windowed higher-lag autocorrelation sequence is used here as an estimate of the power spectrum of the speech signal. This power spectral estimate is processed further (like the well-known Mel frequency cepstral coefficient (MFCC) procedure) by the Mel filter bank, log operation and the discrete cosine transform to get the cepstral coefficients. These cepstral coefficients are referred to as the autocorrelation Mel frequency cepstral coefficients (AMFCCs). We evaluate the speech recognition performance of the AMFCC features on the Aurora and the resource management databases and show that they perform as well as the MFCC features for clean speech and their recognition performance is better than the MFCC features for noisy speech. Finally, we show that the AMFCC features perform better than the features derived from the robust linear prediction-based methods for noisy speech.
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    Journal Title
    Speech Communication
    Volume
    48
    Publisher URI
    http://www.elsevier.com/wps/find/journaldescription.cws_home/505597/description#description
    DOI
    https://doi.org/10.1016/j.specom.2006.08.003
    Subject
    Cognitive and computational psychology
    Linguistics
    Publication URI
    http://hdl.handle.net/10072/14344
    Collection
    • Journal articles

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