  NatiQ is end-to-end text-to-speech system for Arabic. Our speech synthesizer
uses an encoder-decoder architecture with attention. We used both
tacotron-based models (tacotron-1 and tacotron-2) and the faster transformer
model for generating mel-spectrograms from characters. We concatenated
Tacotron1 with the WaveRNN vocoder, Tacotron2 with the WaveGlow vocoder and
ESPnet transformer with the parallel wavegan vocoder to synthesize waveforms
from the spectrograms. We used in-house speech data for two voices: 1) neutral
male "Hamza"- narrating general content and news, and 2) expressive female
"Amina"- narrating children story books to train our models. Our best systems
achieve an average Mean Opinion Score (MOS) of 4.21 and 4.40 for Amina and
Hamza respectively. The objective evaluation of the systems using word and
character error rate (WER and CER) as well as the response time measured by
real-time factor favored the end-to-end architecture ESPnet. NatiQ demo is
available on-line at https://tts.qcri.org
