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 BoussaidEmail author
  • Mohamed Hassine


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.


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



The funding was supported by LARATSI Lab.


  1. Al-Irhayim, Y. F., & Hussein, M. K. (2016). Speech recognition of isolated Arabic words via using wavelet transformation and fuzzy neural network. In Computer engineering and intelligent systems (Vol. 7, No.3).Google Scholar
  2. El Kourd, A. (2014). Arabic isolated word speaker dependent recognition system, Thesis, Islamic University, Gaza, Palestine, Faculty of Engineering, Computer Engineering Department.Google Scholar
  3. El Kourd, A., & El Kourd, K. (2016). Arabic isolated word speaker dependent recognition system. British Journal of Mathematics & Computer Science 1–15.Google Scholar
  4. Elouahabi, S., Atounti, M., Bellouki, M. (2016). Amazigh Isolated Word Speech Recognition System Using Hidden Markov Model Toolkit (HTK). In IEEE Xplore, International Conference on Information Technology for Organizations Development (IT4OD) (pp. 1–7).
  5. Hamdan, S., & Shaout, A. (2016). Hybrid Arabic speech recognition system using FFT, fuzzy logic and neural network, IRACST: International Journal of Computer Science and Information Technology & Security (IJCSITS), 6(No.4).Google Scholar
  6. Hassine, M., Boussaid, L., & Massaoud, H. (2016). Maghrebian dialect recognition based on support vector machines and neural network classifiers. International Journal of Speech Technology 19(4), 687–695.Google Scholar
  7. Haykin, S. (2009). Neural networks and learning machines. New York: Prentice Hall.Google Scholar
  8. Murphy, A. (2014). Implementing speech recognition with artificial neural networks. Ontario: Algoma University Sault Ste. Marie.Google Scholar
  9. Perera, K. A. D., Ranathunga, R. A. D. S., Welivyitigoda, I. P., & Withanawasaw, R. M. (2005). Isolated word recognition. In International Conference on Information and Automation, December15–18, Colombo, Sri Lanka.Google Scholar
  10. Qasim, M., Nawaz, S., Hussain, S., & Habib, T., (2016) Urdu speech recognition system for district names of Pakistan: Development, challenges and solutions. In Conference of the Oriental Chapter of International Committee for Coordination and Standardization of Speech Databases and Assessment Technique (O-COCOSDA), (pp. 26–28). Bali, Indonesia.Google Scholar
  11. Saksamudre, S. K. (2015). Comparative study of isolated word recognition system for Hindi language. IJERT, 4(7), 536–540.Google Scholar
  12. Shlens, J. (2003). A tutorial on principal component analysis derivation, discussion and singular value decomposition, UCSD. Retrieved from
  13. Sukminder, S. G., & Dinesh, K. (2010). Isolated word recognition system for English language. International Journal of Information Technology and Knowledge Management, July-December, 2(2), 447–450.Google Scholar
  14. Venkateswarlu, R. L. K., Kumari, R. V., & Vani Jayasri, G. (2011) Speech recognition using radial basis function neural network. In Proceedings of the 3rd International Conference on Electronics Computer Technology, April 8–10, (pp. 441–445). IEEE Xplore, Kanyakumari, 5941788.
  15. Zarrouk, E., Ben Ayed, Y., & Gargouri, F. (2015). Graphical models for multidialect Arabic isolated word recognition. Procedia Computer Science, 60, 508–516.CrossRefGoogle Scholar

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

Personalised recommendations