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Expressive text-to-speech systems have undergone significant advancementsowing to prosody modeling, but conventional methods can still be improved.Traditional approaches have relied on the autoregressive method to predict thequantized prosody vector; however, it suffers from the issues of long-termdependency and slow inference. This study proposes a novel approach calledDiffProsody in which expressive speech is synthesized using a diffusion-basedlatent prosody generator and prosody conditional adversarial training. Ourfindings confirm the effectiveness of our prosody generator in generating aprosody vector. Furthermore, our prosody conditional discriminatorsignificantly improves the quality of the generated speech by accuratelyemulating prosody. We use denoising diffusion generative adversarial networksto improve the prosody generation speed. Consequently, DiffProsody is capableof generating prosody 16 times faster than the conventional diffusion model.The superior performance of our proposed method has been demonstrated viaexperiments.

DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial Training