  Several recent end-to-end text-to-speech (TTS) models enabling single-stage
training and parallel sampling have been proposed, but their sample quality
does not match that of two-stage TTS systems. In this work, we present a
parallel end-to-end TTS method that generates more natural sounding audio than
current two-stage models. Our method adopts variational inference augmented
with normalizing flows and an adversarial training process, which improves the
expressive power of generative modeling. We also propose a stochastic duration
predictor to synthesize speech with diverse rhythms from input text. With the
uncertainty modeling over latent variables and the stochastic duration
predictor, our method expresses the natural one-to-many relationship in which a
text input can be spoken in multiple ways with different pitches and rhythms. A
subjective human evaluation (mean opinion score, or MOS) on the LJ Speech, a
single speaker dataset, shows that our method outperforms the best publicly
available TTS systems and achieves a MOS comparable to ground truth.
