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.

本篇论文提出了一种使用正则化流和对抗式训练改进的变分推断的并行端到端的语音合成方法，还引入了基于随机预测的语音节奏模型来解决单一文本多样合成的问题。主题涉及文本转语音、端到端模型、变分推断、随机预测、生成模型。相关实验显示本方法表现优于其他公开数据集上的最佳 TTS 系统，并能达到类似于真实语音自然度的效果。