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

Understand

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.