Comparative experiments of different aspects of syllables for robust automatic speech recognition

Abstract

In this paper, monosyllables 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 monosyllables outperformed the rate obtained using trisyllables by 24.76% in the noisy environment. Also, we show in this paper that the integration of a pre-processing enhancement technique in the front-end of the monosyllable-based ASR engine leads to an improvement of the recognition rate by 30.8% compared to the rates obtained using trisyllable-based ASR.