  Recent advances in deep learning methods have elevated synthetic speech
quality to human level, and the field is now moving towards addressing prosodic
variation in synthetic speech.Despite successes in this effort, the
state-of-the-art systems fall short of faithfully reproducing local prosodic
events that give rise to, e.g., word-level emphasis and phrasal structure. This
type of prosodic variation often reflects long-distance semantic relationships
that are not accessible for end-to-end systems with a single sentence as their
synthesis domain. One of the possible solutions might be conditioning the
synthesized speech by explicit prosodic labels, potentially generated using
longer portions of text. In this work we evaluate whether augmenting the
textual input with such prosodic labels capturing word-level prominence and
phrasal boundary strength can result in more accurate realization of sentence
prosody. We use an automatic wavelet-based technique to extract such labels
from speech material, and use them as an input to a tacotron-like synthesis
system alongside textual information. The results of objective evaluation of
synthesized speech show that using the prosodic labels significantly improves
the output in terms of faithfulness of f0 and energy contours, in comparison
with state-of-the-art implementations.
