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Arabic Speech Recognition Using Recurrent Neural Networks M. M. El Choubassi, H. E. El Khoury, C. E. Jabra Alagha, J. A. Skaf and M. A. Al-
 

Summary: Arabic Speech Recognition Using Recurrent Neural Networks
M. M. El Choubassi, H. E. El Khoury, C. E. Jabra Alagha, J. A. Skaf and M. A. Al-
Alaoui
Electrical and Computer Engineering Department
Faculty of Engineering and Architecture American University of Beirut
Beirut 1107 2020, P.O. Box: 11-0236, LEBANON
adnan@aub.edu.lb
Abstract
In this paper, a novel approach for implementing Arabic
isolated speech recognition is described. While most of
the literature on speech recognition (SR) is based on
hidden Markov models (HMM), the present system is
implemented by modular recurrent Elman neural net-
works (MRENN).
The promising results obtained through this design show
that this new neural networks approach can compete
with the traditional HMM-based speech recognition ap-
proaches.
Keywords
Arabic speech recognition, cepstral feature extraction,

  

Source: Al-Alaoui, Mohamad Adnan - Faculty of Engineering and Architecture, American University of Beirut, Lebanon

 

Collections: Engineering