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Effect of gaussian densities and amount of training data on grapheme-based acoustic modeling for Arabic

Grapheme-based acoustic modeling for Arabic is a demanding research area since high phonetic transcription accuracy is not yet solved completely. In this paper, we are studying the use of a pure grapheme-based approach using Gaussian mixture model to implicitly model missing diacritics and investigating the effect of Gaussian densities and amount of training data on speech recognition accuracy. Two transcription systems were built: a phoneme-based system and a grapheme-based system. Several acoustic models were created with each system by changing the number of Gaussian densities and the amount of training data. Results show that by increasing the number of Gaussian densities or the amount of training data, the improvement rate in the grapheme-based approach was found to be faster than in the phoneme-based approach. Hence the accuracy gap between the two approaches can be compensated by increasing either the number of Gaussian densities or the amount of training data.