• DocumentCode
    1533198
  • Title

    Dynamic Features in the Linear-Logarithmic Hybrid Domain for Automatic Speech Recognition in a Reverberant Environment

  • Author

    Ichikawa, Osamu ; Fukuda, Takashi ; Nishimura, Masafumi

  • Author_Institution
    IBM Res. - Tokyo, Yamato, Japan
  • Volume
    4
  • Issue
    5
  • fYear
    2010
  • Firstpage
    816
  • Lastpage
    823
  • Abstract
    Static and dynamic features using Mel frequency cepstral coefficients (MFCCs) are widely used in automatic speech recognition. Since the MFCCs are calculated from logarithmic spectra, the delta and delta-delta are considered to be difference operations in the logarithmic domain. In a reverberant environment, speech signals have late reverberations, whose power is plotted as a long-term exponential decay. This tends to cause the logarithmic delta to keep the constant value for a long time. This paper considers new schemes for calculating delta and delta-delta features that quickly diminish in the reverberant segments. Experiments using the evaluation framework for reverberant environments (CENSREC-4) showed significant improvements by simply replacing the MFCC dynamic features with the proposed dynamic features.
  • Keywords
    cepstral analysis; reverberation; speech recognition; CENSREC-4; MFCC; Mel frequency cepstral coefficients; automatic speech recognition; linear logarithmic hybrid domain; logarithmic delta-delta speech features; speech signal reverberations; Automatic speech recognition; Cepstral analysis; Discrete cosine transforms; Hidden Markov models; Mel frequency cepstral coefficient; Microphone arrays; Noise cancellation; Reverberation; Robustness; Transfer functions; Delta; Mel frequency cepstral coefficient (MFCC); dynamic feature; feature extraction; reverberation; robustness; speech recognition;
  • fLanguage
    English
  • Journal_Title
    Selected Topics in Signal Processing, IEEE Journal of
  • Publisher
    ieee
  • ISSN
    1932-4553
  • Type

    jour

  • DOI
    10.1109/JSTSP.2010.2057191
  • Filename
    5508342