Papers › Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speech
Jaehyeon Kim, Jungil Kong, Juhee Son
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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.
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Code
Syntology Ran 31 of 77 code samples harvested from 9 repositories linked to this paper; 46 have no recorded run. Of those that ran: 2 ran · honoured contract; 6 ran · our draft was wrong; 4 ran · fixture could not drive it; 19 ran with no contract checked.
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Code Syntology ran Syntology
77 samples harvested; 31 ran; 2 honoured the contract we drafted; 46 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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