Hybrid techniques for Arabic letter recognition

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.