H. Tolba

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The presence of noise degrades the recognition percent of automatic speech recognition systems. The improvement of noise can be achieved by changing acoustic units during the recognition process. In this paper, we concentrate on automatic Arabic speech recognition in different conditions of noise using different acoustic units. Automatic Arabic speech was(More)
The presence of noise degrades the recognition percent of automatic speech recognition systems. In this paper, we concentrate on the comparison between Arabic and English digits in different conditions of noise using different acoustic units. Speaker-independent hidden Markov models (HMMs)-based speech recognition system was designed using Hidden Markov(More)
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(More)
This paper addresses the problem of noise robustness of automatic speech recognition (ASR) systems, using a hybrid technique: a speech pre-processing enhancement technique and the use of the syllables as the acoustic units for the ASR process. The speech pre-processing enhancement technique was accomplished by the use of the Ephraim-Malah filter. We tested(More)
This paper presents an evaluation of the use of different acoustic units for automatic speech recognition (ASR). Comparative experiments have indicated that the use of syllables as acoustic units leads to an improvement in the recognition performance of HMM-based ASR systems in noisy environments. The Hidden Markov Model Toolkit (HTK) was used throughout(More)
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(More)
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