Syllable-based automatic Arabic speech recognition
Mohamed M. Azmi, Hesham Tolba, Sherif Mahdy, Mervat Fashal · International Conference on Signal Processing · 2008
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.