  Producing synthetic voice, similar to human-like sound, is an emerging
novelty of modern interactive media systems. Text-To-Speech (TTS) systems try
to generate synthetic and authentic voices via text input. Besides, well known
and familiar dubbing, announcing and narrating voices, as valuable possessions
of any media organization, can be kept forever by utilizing TTS and Voice
Conversion (VC) algorithms . The emergence of deep learning approaches has made
such TTS systems more accurate and accessible. To understand TTS systems
better, this paper investigates the key components of such systems including
text analysis, acoustic modelling and vocoding. The paper then provides details
of important state-of-the-art TTS systems based on deep learning. Finally, a
comparison is made between recently released systems in term of backbone
architecture, type of input and conversion, vocoder used and subjective
assessment (MOS). Accordingly, Tacotron 2, Transformer TTS, WaveNet and
FastSpeech 1 are among the most successful TTS systems ever released. In the
discussion section, some suggestions are made to develop a TTS system with
regard to the intended application.
