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Expressive human speech generally abounds with rich and flexible speechprosody variations. The speech prosody predictors in existing expressive speechsynthesis methods mostly produce deterministic predictions, which are learnedby directly minimizing the norm of prosody prediction error. Its unimodalnature leads to a mismatch with ground truth distribution and harms the model'sability in making diverse predictions. Thus, we propose a novel prosodypredictor based on the denoising diffusion probabilistic model to takeadvantage of its high-quality generative modeling and training stability.Experiment results confirm that the proposed prosody predictor outperforms thedeterministic baseline on both the expressiveness and diversity of predictionresults with even fewer network parameters.

Diverse and Expressive Speech Prosody Prediction with Denoising Diffusion Probabilistic Model