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A large part of the expressive speech synthesis literature focuses onlearning prosodic representations of the speech signal which are then modeledby a prior distribution during inference. In this paper, we compare differentprior architectures at the task of predicting phoneme level prosodicrepresentations extracted with an unsupervised FVAE model. We use bothsubjective and objective metrics to show that normalizing flow based priornetworks can result in more expressive speech at the cost of a slight drop inquality. Furthermore, we show that the synthesized speech has highervariability, for a given text, due to the nature of normalizing flows. We alsopropose a Dynamical VAE model, that can generate higher quality speech althoughwith decreased expressiveness and variability compared to the flow basedmodels.

Predicting phoneme-level prosody latents using AR and flow-based Prior Networks for expressive speech synthesis