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Speech Recognition Based on Feature Extraction with Variable Rate Frequency Sampling

  • Ilyas Potamitis
  • Nikos Fakotakis
  • George Kokkinakis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2166)

Abstract

Most feature extraction techniques involve in their primary stage a Discrete Fourier Transform (DFT) of consecutive, short, overlapping windows. The spectral resolution of the DFT representation is uniform and is given by °f = 2π/N where N is the length of the window The present paper investigates the use of non-uniform rate frequency sampling, varying as a function of the spectral characteristics of each frame, in the context of Automatic Speech Recognition. We are motivated by the non-uniform spectral sensitivity of human hearing and the necessity for a feature extraction technique that auto-focuses on most reliable parts of the spectrum in noisy cases.

Keywords

Speech Recognition Automatic Speech Recognition Feature Extraction Technique Word Recognition Accuracy Noisy Situation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Bourlard H., Hermansky H., Morgan N., “Towards increasing speech recognition error rates”, Speech Communication, 1996, 18, pp. 205–231.CrossRefGoogle Scholar
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    Zhu Q., Alwan A., “On the use of variable frame rate analysis in speech recognition”, ICASSP, 2000, pp. 3264–3267.Google Scholar
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    Vaseghi S., “Advanced Signal Processing and Digital Noise Reduction” Wiley Teubner, 1996.Google Scholar
  4. 4.
    Deller J., Proakis J., Hansen J., “Discrete-Time Processing of Speech Signals”, Prentice Hall, 1987.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ilyas Potamitis
    • 1
  • Nikos Fakotakis
    • 1
  • George Kokkinakis
    • 1
  1. 1.Wire Communications Lab., Electrical & Computer Engineering Dept.University of PatrasPatrasGreece

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