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Biologically inspired Continuous Arabic Speech Recognition

  • N. Hmad
  • T. Allen
Conference paper

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.

Keywords

Speech Recognition Speech Signal Speech Recognition System Echo State Network Continuous Speech Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London 2012

Authors and Affiliations

  1. 1.Nottingham Trent UniversityNottinghamUK

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