  Recent researches on both utterance-level and phone-level prosody modelling
successfully improve the voice quality and naturalness in text-to-speech
synthesis. However, most of them model the prosody with a unimodal distribution
such like a single Gaussian, which is not reasonable enough. In this work, we
focus on phone-level prosody modelling where we introduce a Gaussian mixture
model(GMM) based mixture density network. Our experiments on the LJSpeech
dataset demonstrate that GMM can better model the phone-level prosody than a
single Gaussian. The subjective evaluations suggest that our method not only
significantly improves the prosody diversity in synthetic speech without the
need of manual control, but also achieves a better naturalness. We also find
that using the additional mixture density network has only very limited
influence on inference speed.
