  Text does not fully specify the spoken form, so text-to-speech models must be
able to learn from speech data that vary in ways not explained by the
corresponding text. One way to reduce the amount of unexplained variation in
training data is to provide acoustic information as an additional learning
signal. When generating speech, modifying this acoustic information enables
multiple distinct renditions of a text to be produced.
  Since much of the unexplained variation is in the prosody, we propose a model
that generates speech explicitly conditioned on the three primary acoustic
correlates of prosody: $F_{0}$, energy and duration. The model is flexible
about how the values of these features are specified: they can be externally
provided, or predicted from text, or predicted then subsequently modified.
  Compared to a model that employs a variational auto-encoder to learn
unsupervised latent features, our model provides more interpretable,
temporally-precise, and disentangled control. When automatically predicting the
acoustic features from text, it generates speech that is more natural than that
from a Tacotron 2 model with reference encoder. Subsequent human-in-the-loop
modification of the predicted acoustic features can significantly further
increase naturalness.
