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 (VITS)

Jaehyeon Kim, Jungil Kong, Juhee Son

Abstract

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.

AI research summary

The paper presents a novel end-to-end TTS system that leverages variational inference and adversarial learning to produce high-quality, natural-sounding speech. The combination of these methodologies represents a significant advancement in the field of speech synthesis, with potential applications across various domains.

AI methodology analysis

The proposed methodology integrates a conditional variational autoencoder (VAE) with adversarial training to create a novel end-to-end text-to-speech (TTS) system. The use of normalizing flows enhances the expressive power of the latent variables, while the stochastic duration predictor addresses the one-to-many relationship in speech synthesis. This combination allows for the generation of diverse speech outputs that reflect natural variations in pitch and rhythm. The architecture is well-structured, employing a posterior encoder, prior encoder, decoder, and discriminator, which collectively improve synthesis quality and efficiency. The method's reliance on variational inference and adversarial training is a significant advancement in TTS technology.

AI results analysis

The experimental evaluation is robust, utilizing two datasets (LJ Speech and VCTK) to assess the performance of the proposed model against existing TTS systems. The use of mean opinion scores (MOS) for subjective evaluation provides a clear metric for assessing audio quality. The results demonstrate that the proposed model outperforms existing systems, achieving a MOS comparable to ground truth. Additionally, the ablation studies effectively highlight the contributions of various components of the model, such as the normalizing flow and the stochastic duration predictor.

AI reproducibility analysis

The paper provides sufficient implementation details, including architecture specifications and training procedures, which are crucial for reproducibility. The authors have made their source code and demo available, further facilitating the ability of other researchers to replicate their findings.

AI limitations analysis

While the proposed model shows significant improvements over existing systems, it still relies on certain preprocessing steps, such as text normalization and phonemization, which could limit its applicability in more diverse contexts. Additionally, the paper does not extensively address the computational resources required for training and inference, which may be a barrier for some users.

AI broader impact analysis

The advancements presented in this paper have the potential to significantly improve the quality and efficiency of TTS systems, making them more accessible for applications in voice assistants, audiobooks, and other areas where natural-sounding speech is essential. The integration of a stochastic duration predictor could lead to more expressive and human-like speech synthesis, enhancing user experience in various applications. The paper presents a novel end-to-end TTS system that leverages variational inference and adversarial learning to produce high-quality, natural-sounding speech. The combination of these methodologies represents a significant advancement in the field of speech synthesis, with potential applications across various domains.