Synthesized speech can be created by concatenating units of recorded speech that are stored in a database as in [1] [2]. Common units used in concatenative synthesizers are phones or diphones. Alternatively, statistical parametric synthesizers also known as HMM-based synthesizers (based on hidden Markov models) can be used to create the synthesized speech [3][4]. In these systems, the frequency spectrum (vocal tract), fundamental frequency (voice source), and duration (prosody) of speech are modeled simultaneously by HMMs. Speech waveforms are generated from HMMs themselves based on the maximum likelihood criterion. Recently, neural networks have been used as acoustic models for statistical parametric synthesizers [5]. In addition, end-to-end DNN-based speech synthesizers such as Tacotron [6] by Google and Deep Voice [7] from Baidu are an active area of research. A state-of-the-art synthesizer based on Tacotron, developed for the Arabic language, is available on github [8].

Since Modern Standard Arabic (MSA) is written without diacritics, the first step to develop an Arabic TTS engine [2] is to restore the diacritics of each word in the text [9][10][11]. The diacritized text is then passed to a phonetic transcription module to generate the phoneme sequence for each phrase [12]. Hence, a synthesizer (i.e. concatenative, parametric, neural networks) can be used to synthesis the speech.