Syllable-based automatic Arabic speech recognition

Authors:
Mohamed Mostafa Azmi;Hesham Tolba;Sherif Mahdy;Mervat Fashal
Affiliations:
Elect. Eng. Dept., Alexandria Higher Institute of Engineering, Alexandria University, Alexandria, Egypt;Elect. Eng. Dept., Faculty of Engineering, Alexandria University, Alexandria, Egypt;IT Dept., Faculty of Information Technology, Cairo University, Alexandria, Egypt;Phonetics Dept., Faculty of Arts., Alexandria University, Alexandria, Egypt
Venue:
ISPRA'08 Proceedings of the 7th WSEAS International Conference on Signal Processing, Robotics and Automation
Year:
2008

Citing 3
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Abstract

In this paper, we concentrate on the automatic recognition of Egyptian Arabic speech using syllables. Arabic spoken digits were described by showing their constructing phonemes, triphones, syllables and words. Speaker-independent hidden markov models (HMMs)-based speech recognition system was designed using Hidden markov model toolkit (HTK). The database used for both training and testing consists from forty-four Egyptian speakers. Experiments show that the recognition rate using syllables outperformed the rate obtained using monophones, triphones and words by 2.68%, 1.19% and 1.79% respectively. A syllable unit spans a longer time frame, typically three phones, thereby offering a more parsimonious framework for modeling pronunciation variation in spontaneous speech. Moreover, syllable-based recognition has relatively smaller number of used units and runs faster than word-based recognition.