Expressive text-to-speech systems have undergone significant advancements
owing to prosody modeling, but conventional methods can still be improved.
Traditional approaches have relied on the autoregressive method to predict the
quantized prosody vector; however, it suffers from the issues of long-term
dependency and slow inference. This study proposes a novel approach called
DiffProsody in which expressive speech is synthesized using a diffusion-based
latent prosody generator and prosody conditional adversarial training. Our
findings confirm the effectiveness of our prosody generator in generating a
prosody vector. Furthermore, our prosody conditional discriminator
significantly improves the quality of the generated speech by accurately
emulating prosody. We use denoising diffusion generative adversarial networks
to improve the prosody generation speed. Consequently, DiffProsody is capable
of generating prosody 16 times faster than the conventional diffusion model.
The superior performance of our proposed method has been demonstrated via
experiments.

通过使用基于扩散的潜在韵律生成器和韵律条件对抗训练的新方法 DiffProsody，本研究证实了其在生成韵律向量方面的有效性，并且韵律条件鉴别器通过准确模拟韵律极大提高了生成语音的质量。使用去噪扩散生成对抗网络提高了韵律生成的速度，因此 DiffProsody 能够比传统的扩散模型生成韵律的速度快 16 倍。通过实验证明了我们提出的方法具有卓越的性能。