  Prosody modeling is an essential component in modern text-to-speech (TTS)
frameworks. By explicitly providing prosody features to the TTS model, the
style of synthesized utterances can thus be controlled. However, predicting
natural and reasonable prosody at inference time is challenging. In this work,
we analyzed the behavior of non-autoregressive TTS models under different
prosody-modeling settings and proposed a hierarchical architecture, in which
the prediction of phoneme-level prosody features are conditioned on the
word-level prosody features. The proposed method outperforms other competitors
in terms of audio quality and prosody naturalness in our objective and
subjective evaluation.
