Abstract
A phoneme identification system for Arabic language has been developed. It is based on a hybrid approach that incorporates two levels of phoneme identification. In the first layer power spectral information, efficiently condensed through the use of singular value decomposition, is utilized to train separate self-organizing maps for identifying each Arabic phoneme. This is followed by a second layer of identification, based on similarity metric, that compares the standard pitch contours of phonemes with the pitch contours of the input sound. The second layer performs the identification in case the first layer generates multiple classifications of the same input sound. The system has been developed using utterances of twenty-eight Arabic phonemes from over a hundred speakers. The identification accuracy based on the first layer alone was recorded at 71%, which increased to 91% with the addition of the second identification layer. The introduction of singular values for training instead of power spectral densities directly has resulted in reduction of training and recognition times for self-organizing maps by 80% and 89% respectively. The research concludes that power spectral densities along with the pitch information result in an acceptable and robust identification system for the Arabic phonemes.
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Awais, M.M., Masud, S., Shamail, S., Akhtar, J. (2004). A Hybrid Multi-layered Speaker Independent Arabic Phoneme Identification System. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_61
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DOI: https://doi.org/10.1007/978-3-540-28651-6_61
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