Title :
Arabic speech recognition using recurrent neural networks
Author :
El Choubassi, M.M. ; El Khoury, H.E. ; Alagha, C. E Jabra ; Skaf, J.A. ; Al-Alaoui, M.A.
Author_Institution :
Dept. of Electr. & Comput. Eng., American Univ. of Beirut, Lebanon
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 networks (MRENN). The promising results obtained through this design show that this new neural networks approach can compete with the traditional HMM-based speech recognition approaches.
Keywords :
hidden Markov models; natural languages; recurrent neural nets; speech recognition; Arabic speech recognition; HMM; hidden Markov model; modular recurrent Elman neural network; Automatic speech recognition; Cepstral analysis; Feature extraction; Hidden Markov models; Neural networks; Recurrent neural networks; Speech recognition; Strontium; Vector quantization; Vocabulary;
Conference_Titel :
Signal Processing and Information Technology, 2003. ISSPIT 2003. Proceedings of the 3rd IEEE International Symposium on
Print_ISBN :
0-7803-8292-7
DOI :
10.1109/ISSPIT.2003.1341178
