Syllable-based automatic Arabic speech recognition in noisy enviroment

In this paper, syllables are proposed to be used as acoustic units to improve the performance of automatic speech recognition (ASR) systems of Arabic spoken proverbs in noisy environments. To test our proposed approach, a speaker-independent HMM-based speech recognition system was designed using Hidden Markov Model Toolkit (HTK). A series of experiments on noisy speech has been carried out using an Arabic database that consists of fifty-nine Egyptian speakers. The obtained results show that the recognition rate using syllables outperformed the rate, obtained using monophones and triphones by 20.88% and 15.82%, respectively. The use of syllables did not only improve the performance of the ASR process in noisy environments, but also it limited the complexity of the computation (and consequently the running time) of the recognition process. Also, we show in this paper that the integration of a pre-processing enhancement technique in the front-end of the syllable-based ASR engine leads to an improvement of the recognition rate by 20.88% and 15.82%, compared to the rates obtained using monophones and triphone-based ASR, respectively.