  Speech synthesis is the artificial production of human speech. A typical
text-to-speech system converts a language text into a waveform. There exist
many English TTS systems that produce mature, natural, and human-like speech
synthesizers. In contrast, other languages, including Arabic, have not been
considered until recently. Existing Arabic speech synthesis solutions are slow,
of low quality, and the naturalness of synthesized speech is inferior to the
English synthesizers. They also lack essential speech key factors such as
intonation, stress, and rhythm. Different works were proposed to solve those
issues, including the use of concatenative methods such as unit selection or
parametric methods. However, they required a lot of laborious work and domain
expertise. Another reason for such poor performance of Arabic speech
synthesizers is the lack of speech corpora, unlike English that has many
publicly available corpora and audiobooks. This work describes how to generate
high quality, natural, and human-like Arabic speech using an end-to-end neural
deep network architecture. 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 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.
