N ati Q : An End-to-end Text-to-Speech System for A rabic (2022)

Abstract: 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) neu- tral 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 aver- age 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 online at https://tts.qcri.org .

## system architecture our natiq system is a web-based demonstration that is composed of two main components: ## evaluation to evaluate the performance of each of the models, we built an evaluation test set composed of 100 sentences of varying lengths, collected from six domains including: culture, economy, literature, politics, sports, and technology. the sentences were collected between jan 1st to jan 20th, 2022. they include excerpts from current topics and news. we decoded each sentence using the models and for each of the voices. this resulted in a pool of 600 audio files to evaluate. we carried automatic and manual (subjective) evaluations described below: ## conclusion we presented natiq arabic text-to-speech system, a system based on end-to-end framework that combines transformer encoder-decoder and wavegan vocoder. the system was evaluated using subjective metric, mean opinion score and objective speed, wer and cer. the system achieved a mos of 4.35 and 4.72 for amina and hamza respectively. such performance is very comparable to english systems @xcite . for the expressive speaker, the performance of the system still lags behind the neutral one. this is due to the complex and rich features encoded in expressive voice. we plan to explore different techniques that exploits the additional features in the voice such as @xcite which aim to combine frames and style information as two objective functions to optimize while training the model.