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International Journal of Speech Technology

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16-08-2016

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

Authors: Mohamed Hassine, Lotfi Boussaid, Hassani Messaoud

Published in: International Journal of Speech Technology | Issue 4/2016

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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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Title: Maghrebian dialect recognition based on support vector machines and neural network classifiers
Authors: Mohamed Hassine
Lotfi Boussaid
Hassani Messaoud
Publication date: 16-08-2016
Publisher: Springer US
Published in: International Journal of Speech Technology / Issue 4/2016
Print ISSN: 1381-2416
Electronic ISSN: 1572-8110
DOI: https://doi.org/10.1007/s10772-016-9360-6

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