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Maghrebian dialect recognition based on support vector machines and neural network classifiers

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Abstract

This paper investigates the feed forward back propagation neural network (FFBPNN) and the support vector machine (SVM) for the classification of two Maghrebian dialects: Tunisian and Moroccan. The dialect used by the Moroccan speakers is called “La Darijja” and that of Tunisians is called “Darija”. An Automatic Speech Recognition System is implemented in order to identify ten Arabic digits (from zero to nine). The implementation of our present system consists of two phases: The features extraction using a variety of popular hybrid techniques and the classification phase using separately the FFBPNN and the SVM. The experimental results showed that the recognition rates with both approaches have reached 98.3 % with FFBPNN and 97.5 % with SVM.

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Authors and Affiliations

  1. Laboratory of Automatic Signal and Image Processing, National School of Engineers of Monastir, University of Monastir, 5019, Monastir, Tunisia

    Mohamed Hassine & Hassani Messaoud

  2. Laboratory of Electronics and Microelectronics, Faculty of Science of Monastir, University of Monastir, 5019, Monastir, Tunisia

    Lotfi Boussaid

Authors
  1. Mohamed Hassine
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  2. Lotfi Boussaid
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  3. Hassani Messaoud
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Correspondence to Lotfi Boussaid.

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Hassine, M., Boussaid, L. & Messaoud, H. Maghrebian dialect recognition based on support vector machines and neural network classifiers. Int J Speech Technol 19, 687–695 (2016). https://doi.org/10.1007/s10772-016-9360-6

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  • DOI: https://doi.org/10.1007/s10772-016-9360-6

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