Automatic dialect identification of spoken Arabic speech using deep neural networks
Joint Authors
Husayn, Widad
Badr, Najwa L.
Muhammad, Muna Abd al-Azim
Source
International Journal of Intelligent Computing and Information Sciences
Issue
Vol. 22, Issue 4 (31 Dec. 2022), pp.25-34, 10 p.
Publisher
Ain Shams University Faculty of Computer and Information Sciences
Publication Date
2022-12-31
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
Dialect identification is considered a subtask of the language identification problem and it is thought to be a more complex case due to the linguistic similarity between different dialects of the same language.
in this paper, a novel approach is introduced for identifying three of the most used Arabic dialects : Egyptian, Levantine, and gulf dialects.
in this study, four experiments were conducted using different classification approaches that vary from simple classifiers such as Gaussian naïve Bayes and support vector machines to more complex classifiers using deep neural networks (DNN).
a features vector of 13 Mel cepstral coefficients (MFCCs) of the audio signals was used to train the classifiers using a multi-dialect parallel corpus.
the experimental results showed that the proposed convolutional neural networks-based classifier has outperformed other classifiers in all three dialects.
it has achieved an average improvement of 0.16, 0.19, and 0.19 in the Egyptian dialect, and of 0.07, 0.13, and 0.1 in the Gulf dialect, and of 0.52, 0.35, and 0.49 in the Levantine dialect for the precision, recall and f1-score metrics respectively.
American Psychological Association (APA)
Muhammad, Muna Abd al-Azim& Husayn, Widad& Badr, Najwa L.. 2022. Automatic dialect identification of spoken Arabic speech using deep neural networks. International Journal of Intelligent Computing and Information Sciences،Vol. 22, no. 4, pp.25-34.
https://search.emarefa.net/detail/BIM-1444912
Modern Language Association (MLA)
Muhammad, Muna Abd al-Azim…[et al.]. Automatic dialect identification of spoken Arabic speech using deep neural networks. International Journal of Intelligent Computing and Information Sciences Vol. 22, no. 4 (Dec. 2022), pp.25-34.
https://search.emarefa.net/detail/BIM-1444912
American Medical Association (AMA)
Muhammad, Muna Abd al-Azim& Husayn, Widad& Badr, Najwa L.. Automatic dialect identification of spoken Arabic speech using deep neural networks. International Journal of Intelligent Computing and Information Sciences. 2022. Vol. 22, no. 4, pp.25-34.
https://search.emarefa.net/detail/BIM-1444912
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 33-34
Record ID
BIM-1444912