Abstract
Despite many years of research into speech recognition systems, there are limited research publications available covering Arabic speech recognition. Although statistical techniques have been the most applied techniques for such classification problems, Neural Networks have also recorded successful results in speech recognition. In this research three different biologically inspired Continuous Arabic Speech Recognition neural network system structures are presented. An Arabic phoneme database (APD) of six male speakers was constructed manually from the King Abdulaziz Arabic Phonetics Database (KAPD). The Mel-Frequency Cepstrum Coefficients (MFCCs) algorithm was used to extract the phoneme features from the speech signals of this database. The normalized dataset was used to train and test three different architectures of Multilayer Perceptron (MLP) neural network identification systems.
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Hmad, N., Allen, T. (2012). Biologically inspired Continuous Arabic Speech Recognition. In: Bramer, M., Petridis, M. (eds) Research and Development in Intelligent Systems XXIX. SGAI 2012. Springer, London. https://doi.org/10.1007/978-1-4471-4739-8_20
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DOI: https://doi.org/10.1007/978-1-4471-4739-8_20
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