  In this paper, we introduce Kathaka, a model trained with a novel two-stage
training process for neural speech synthesis with contextually appropriate
prosody. In Stage I, we learn a prosodic distribution at the sentence level
from mel-spectrograms available during training. In Stage II, we propose a
novel method to sample from this learnt prosodic distribution using the
contextual information available in text. To do this, we use BERT on text, and
graph-attention networks on parse trees extracted from text. We show a
statistically significant relative improvement of $13.2\%$ in naturalness over
a strong baseline when compared to recordings. We also conduct an ablation
study on variations of our sampling technique, and show a statistically
significant improvement over the baseline in each case.
