Existing Arabic speech synthesis solutions are slow, of low quality, and the naturalness of synthesized speech is inferior to the English . They also lack essential speech key factors such as intonation, stress, and rhythm . This work describes how to generate high quality, natural, and human-like Arabic speech using an end-to-end neural deep network architecture . It illustrates how to use English character embedding despite using diacritic Arabic characters as input and how to preprocess these audio samples to achieve the best results. This work uses just $langle$ text, audio $rangle$ pairs with a relatively small amount of recorded audio samples with a total of 2.41 hours. It shows how to . use English characters despite . using Arabic characters . The work illustrates how . to use Arabic character embeding despite using diacrit Arabic characters as input to achieve . the best result. It illustrates . using English character embeddedding. It also demonstrates how to create high quality and low-quality Arabic speech. It

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Keywords : arabic - speech - characters - quality - english -

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