  Generating natural speech with diverse and smooth prosody pattern is a
challenging task. Although random sampling with phone-level prosody
distribution has been investigated to generate different prosody patterns, the
diversity of the generated speech is still very limited and far from what can
be achieved by human. This is largely due to the use of uni-modal distribution,
such as single Gaussian, in the prior works of phone-level prosody modelling.
In this work, we propose a novel approach that models phone-level prosodies
with GMM based mixture density network (GMM-MDN). Experiments on the LJSpeech
dataset demonstrate that phone-level prosodies can precisely control the
synthetic speech and GMM-MDN can generate more natural and smooth prosody
pattern than a single Gaussian. Subjective evaluations further show that the
proposed approach not only achieves better naturalness, but also significantly
improves the prosody diversity in synthetic speech without the need of manual
control.
