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In this paper, we have proposed a new technique of Arabic Speech Recognition (ASR) with monolocutor 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. The second step consists in features extracting from those recorded words. The third step is to classify those extracted features. This extraction is performed by computing at first step, the Mel Frequency Cepstral Coefficients (MFCCs) from each recorded word, then the Bionic Wavelet Transform (BWT) is applied to the vector obtained from the concatenation of the computed MFCCs. The obtained bionic wavelet coefficients are 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 and each of them is repeated twenty five times by the same locutor. A simulation program is performed to test the performance of the proposed technique and shows a classification rate equals to 99.39%. We have also introduced a module of denoising as a phase of preprocessing. In this denoising module, we have treated the case of white noise and we have used the Wiener filtering. In case of SNR=5dB, the obtained recognition rate is equals to 78.7% and in case of SNR=10dB, it is equals to 93.9%.