Arabic Speech Recognition by MFCC and Bionic Wavelet Transform Using a Multi Layer Perceptron for Voice Control

In this paper, we have proposed a new technique of Arabic speech recognition with mono-locutor and a reduced vocabulary. This technique consists at first step in using our proper speech database containing Arabic speech words which are recorded by a mono-locutor for a voice command. The second step consists in features extraction from those recorded words. The third step consists in classifying those extracted features. The features extraction is performed by computing at first, the Mel Frequency Cepstral Coefficients (MFCCs) from each recorded word, then the Bionic Wavelet Transform (BWT) was applied to the vector obtained from the concatenation of the obtained MFCCs. The obtained bionic wavelet coefficients were then concatenated to construct one input of a Multi-Layer Perceptual (MLP) used for features classification. In the MLP learning and test phases, we have used eleven Arabic words each if them was repeated twenty five times by the same locutor. A simulation program used to test the performance of the proposed technique showed a classification rate equals to 99.39%.