Hybrid Techniques for Arabic Letter Recognition
International Journal of Intelligent Information Systems
Volume 4, Issue 1, February 2015, Pages: 27-34
Received: Aug. 30, 2014; Accepted: Jan. 22, 2015; Published: Feb. 2, 2015
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Authors
Mohamed Hassine, LARATSI Lab, ENIM, University of Monastir, Monastir, Tunisia
Lotfi Boussaid, EµE Lab, FSM, University of Monastir, Monastir, Tunisia
Hassani Massouad, LARATSI Lab, ENIM, University of Monastir, Monastir, Tunisia
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Abstract
In this paper we investigate the use of the feed-forward back propagation neural networks (FFBPNN) for automatic speech recognition of Arabic letters with their four vowels (Fatha, dhamma, Kasra, Soukoun). This investigation will constitute a basically step for the recognition of continuous Speech. Features were extracted from recorded corpus by using a variety of conventional methods such as Linear Predictive Codes (LPC), Perceptual Linear Prediction (PLP), Relative Spectral Perceptual Linear Prediction (RASTA-PLP), Mel Frequency Cepstral Coefficients (MFCC), Continuous Wavelet Transform (CWT), etc. Here, several hybrid methods have been used too. Since the extracted features have large dimensionalities they were reduced by conserving the most discriminatory information with the Principal Component Analysis (PCA) technique. The recognition performance has been improved particularly when we use the PLP method followed by PCA technique.
Keywords
Speech Recognition, Arabic Letters, Hybrid Techniques, MFCC, PLP, LPCC, PCA and FFBPNN
To cite this article
Mohamed Hassine, Lotfi Boussaid, Hassani Massouad, Hybrid Techniques for Arabic Letter Recognition, International Journal of Intelligent Information Systems. Vol. 4, No. 1, 2015, pp. 27-34. doi: 10.11648/j.ijiis.20150401.14
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