  Previous works on expressive speech synthesis focus on modelling the
mono-scale style embedding from the current sentence or context, but the
multi-scale nature of speaking style in human speech is neglected. In this
paper, we propose a multi-scale speaking style modelling method to capture and
predict multi-scale speaking style for improving the naturalness and
expressiveness of synthetic speech. A multi-scale extractor is proposed to
extract speaking style embeddings at three different levels from the
ground-truth speech, and explicitly guide the training of a multi-scale style
predictor based on hierarchical context information. Both objective and
subjective evaluations on a Mandarin audiobooks dataset demonstrate that our
proposed method can significantly improve the naturalness and expressiveness of
the synthesized speech.
