Arabic phoneme recognition using neural networks

  • Authors:

  • Manal El-Obaid;Amer Al-Nassiri;Iman Abuel Maaly

  • Affiliations:

  • Faculty of Computer Science &Eng., Ajman University of Science & Technology, Ajman, UAE;Faculty of Computer Science & Eng., Ajman University of Science & Technology, Ajman, UAE;Faculty of Engineering, University of Khartoum, Khartoum, Sudan

  • Venue:
  • SIP'06 Proceedings of the 5th WSEAS international conference on Signal processing
  • Year:
  • 2006

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Abstract

The main theme of this paper is the recognition of isolated Arabic speech phonemes using artificial neural networks, as most of the researches on speech recognition (SR) are based on Hidden Markov Models (HMM). The technique in this paper can be divided into three major steps: firstly the preprocessing in which the original speech is transformed into digital form. Two methods for preprocessing have been applied, FIR filter and Normalization. Secondly, the global features of the Arabic speech phoneme are then extracted using Cepstral coefficients, with frame size of 512 samples, 170 overlapping, and hamming window. Finally, recognition of Arabic speech phoneme using supervised learning method and Multi Layer Perceptron Neural Network MLP, based on Feed Forward Backprobagation. The proposed system achieved a recognition rate within 96.3% for most of the 34 phonemes. The database used in this paper is KAPD (King AbdulAziz Phonetics Database), and the algorithms were written in MATLAB.