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International Journal of Speech Technology

, Volume 21, Issue 1, pp 29–37 | Cite as

Arabic isolated word recognition system using hybrid feature extraction techniques and neural network

  • Lotfi Boussaid
  • Mohamed Hassine
Article
  • 108 Downloads

Abstract

In this paper, we implemented a speaker-dependent speech recognition system for 11 standard Arabic isolated words. During the feature extraction phase, several techniques were used such as Mel frequency cepstral coefficients, perceptual linear prediction, relative perceptual linear prediction and their first order temporal derivatives. Principal component analysis was adopted in order to reduce the feature dimension. The recognition phase is based on the feed forward back-propagation neural network using two learning algorithms: the Levenberg–Marquardt “Trainlm” and the scaled conjugate gradient “Trainscg”. Hybrid approaches were used and compared in terms of computational time and recognition rates and have produced very interesting performances.

Keywords

Speech recognition Mel frequency cepstral coefficients Perceptual linear predictive Principal component analysis Feed forward back-propagation neural network 

Notes

Acknowledgements

The funding was supported by LARATSI Lab.

References

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  1. 1.EµE Lab, Ecole Nationale d’Ingénieurs de Monastir (ENIM)University of MonastirMonastirTunisia
  2. 2.LARATSI Lab, Ecole Nationale d’Ingénieurs de Monastir (ENIM)University of MonastirMonastirTunisia

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