Arabic Speech Synthesis using Deep Neural Networks

Text-to-speech (TTS) synthesis is a rapidly growing field of research. Deep learning has shown impressive results in speech synthesis and outperformed the older concatenative and parametric methods. In this paper, speech synthesis using deep learning architectures is explored and two models are utilized in an end-to-end Arabic TTS system. The results of the two systems are compared to concatenative TTS system using the Mean Opinion Score (MOS) of the synthesized speech and indicates that deep learning based systems have outperformed the concatenative system when it comes to naturalness and intelligibility; moreover, it reduces system complexity.