  Prosody is an integral part of communication, but remains an open problem in
state-of-the-art speech synthesis. There are two major issues faced when
modelling prosody: (1) prosody varies at a slower rate compared with other
content in the acoustic signal (e.g. segmental information and background
noise); (2) determining appropriate prosody without sufficient context is an
ill-posed problem. In this paper, we propose solutions to both these issues. To
mitigate the challenge of modelling a slow-varying signal, we learn to
disentangle prosodic information using a word level representation. To
alleviate the ill-posed nature of prosody modelling, we use syntactic and
semantic information derived from text to learn a context-dependent prior over
our prosodic space. Our Context-Aware Model of Prosody (CAMP) outperforms the
state-of-the-art technique, closing the gap with natural speech by 26%. We also
find that replacing attention with a jointly-trained duration model improves
prosody significantly.
