VSE knjižnice (vzajemna bibliografsko-kataložna baza podatkov COBIB.SI)
  • A noise robust feature extraction algorithm using joint wavelet packet subband decomposition and AR modeling of speech signals
    Kotnik, Bojan ; Kačič, Zdravko
    This paper presents a noise robust feature extraction algorithm NRFE using joint wavelet packet decomposition (WPD) and autoregressive (AR) modeling of a speech signal. In opposition to the short ... time Fourier transform (STFT)-based time-frequency signal representation, wavelet packet decomposition can lead to better representation of non-stationary parts of the speech signal (e.g. consonants). The vowels are well described with an AR model as in LPC analysis. The proposed Root-Log compression scheme is used to perform the computation of the wavelet packet parameters. The separately extracted WPD and AR-based parameters are combined together and then transformed with the usage of linear discriminant analysis (LDA) to finally produce a lower dimensional output feature vector. The noise robustness is improved with the application of proposed wavelet-based denoising algorithm with a modified soft thresholding procedure and time-frequency adaptive threshold. The proposed voice activity detector based on a skewness-to-kurtosis ratio of the LPC residual signal is used to effectively perform a frame-dropping principle. The speech recognition results achieved on Aurora 2 and Aurora 3 databases show overall performance improvement of 44.7% and 48.2% relative to the baseline MFCC front-end, respectively.
    Vir: Signal processing. - ISSN 0165-1684 (Vol. 87, no. 6, 2007, str. 1202-1223)
    Vrsta gradiva - članek, sestavni del
    Leto - 2007
    Jezik - angleški
    COBISS.SI-ID - 11170838

    Povezava(-e):

    http://dx.doi.org/10.1016/j.sigpro.2006.10.009
    Digitalna knjižnica Univerze v Mariboru – DKUM

vir: Signal processing. - ISSN 0165-1684 (Vol. 87, no. 6, 2007, str. 1202-1223)
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