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

Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech