Filled Pause Classification Using Energy-Boosted Mel-Frequency Cepstrum Coefficients

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 291)

Abstract

Filled pause is one type of disfluency, identified as the often occurred disfluency in spontaneous speech and known to affect Automatic Speech Recognition accuracy. The purpose of this study is to analyze the impact of boosting Mel-Frequency Cepstral Coefficients with energy feature in classifying filled pause. A total of 828 filled pauses comprising a mixture of 62 male and female speakers are classified into /mhm/, /aaa/ and /eer/. A back-propagation neural network using fusion of gradient descent with momentum and adaptive learning rate is used as the classifier. The results revealed that energy-boosted Mel-Frequency Cepstral Coefficients produced a higher accuracy rate of 77 % in classifying filled pauses.

Keywords

Malay filled pause Energy Mel-frequency cepstral coefficients Energy-boosted MFCC Artificial neural network Gradient descent momentum 

Notes

Acknowledgments

The authors thankfully acknowledge Ministry of Higher Education Malaysia for Fundamental Research Grant Scheme (FRGS, Grant No: 600-RMI/FRGS 5/3(48/2013) and MARA University of Technology for providing research facilities throughout this research.

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Copyright information

© Springer Science+Business Media Singapore 2014

Authors and Affiliations

  • Raseeda Hamzah
    • 1
  • Nursuriati Jamil
    • 1
  • Noraini Seman
    • 1
  1. 1.Faculty of Computer and Mathematical SciencesMARA University of TechnologyShah AlamMalaysia

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