  Neural text-to-speech systems are often optimized on L1/L2 losses, which make
strong assumptions about the distributions of the target data space. Aiming to
improve those assumptions, Normalizing Flows and Diffusion Probabilistic Models
were recently proposed as alternatives. In this paper, we compare traditional
L1/L2-based approaches to diffusion and flow-based approaches for the tasks of
prosody and mel-spectrogram prediction for text-to-speech synthesis. We use a
prosody model to generate log-f0 and duration features, which are used to
condition an acoustic model that generates mel-spectrograms. Experimental
results demonstrate that the flow-based model achieves the best performance for
spectrogram prediction, improving over equivalent diffusion and L1 models.
Meanwhile, both diffusion and flow-based prosody predictors result in
significant improvements over a typical L2-trained prosody models.
