  In neural text-to-speech (TTS), two-stage system or a cascade of separately
learned models have shown synthesis quality close to human speech. For example,
FastSpeech2 transforms an input text to a mel-spectrogram and then HiFi-GAN
generates a raw waveform from a mel-spectogram where they are called an
acoustic feature generator and a neural vocoder respectively. However, their
training pipeline is somewhat cumbersome in that it requires a fine-tuning and
an accurate speech-text alignment for optimal performance. In this work, we
present end-to-end text-to-speech (E2E-TTS) model which has a simplified
training pipeline and outperforms a cascade of separately learned models.
Specifically, our proposed model is jointly trained FastSpeech2 and HiFi-GAN
with an alignment module. Since there is no acoustic feature mismatch between
training and inference, it does not requires fine-tuning. Furthermore, we
remove dependency on an external speech-text alignment tool by adopting an
alignment learning objective in our joint training framework. Experiments on
LJSpeech corpus shows that the proposed model outperforms publicly available,
state-of-the-art implementations of ESPNet2-TTS on subjective evaluation (MOS)
and some objective evaluations.
