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.

翻译：富有表现力的文语转换系统因韵律建模取得了显著进展，但传统方法仍有改进空间。传统方法依赖自回归方法来预测量化韵律向量，然而该方法存在长程依赖和推理速度慢的问题。本研究提出了一种名为DiffProsody的新方法，通过基于扩散的潜在韵律生成器和韵律条件对抗训练来合成富有表现力的语音。我们的实验结果证实了韵律生成器在生成韵律向量方面的有效性。此外，我们的韵律条件判别器通过精确模拟韵律，显著提升了合成语音的质量。我们利用去噪扩散生成对抗网络来提高韵律生成速度。因此，DiffProsody生成韵律的速度比传统扩散模型快16倍。实验证明了我们提出的方法的优越性能。