  Generating natural speech with a 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 humans. 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 a GMM-based mixture density network(MDN) and then
extend it for multi-speaker TTS using speaker adaptation transforms of Gaussian
means and variances. Furthermore, we show that we can clone the prosodies from
a reference speech by sampling prosodies from the Gaussian components that
produce the reference prosodies. Our experiments on LJSpeech and LibriTTS
dataset show that the proposed method with GMM-based MDN not only achieves
significantly better diversity than using a single Gaussian in both
single-speaker and multi-speaker TTS, but also provides better naturalness. The
prosody cloning experiments demonstrate that the prosody similarity of the
proposed method with GMM-based MDN is comparable to recent proposed
fine-grained VAE while the target speaker similarity is better.
