  Previous works on expressive speech synthesis mainly focus on current
sentence. The context in adjacent sentences is neglected, resulting in
inflexible speaking style for the same text, which lacks speech variations. In
this paper, we propose a hierarchical framework to model speaking style from
context. A hierarchical context encoder is proposed to explore a wider range of
contextual information considering structural relationship in context,
including inter-phrase and inter-sentence relations. Moreover, to encourage
this encoder to learn style representation better, we introduce a novel
training strategy with knowledge distillation, which provides the target for
encoder training. Both objective and subjective evaluations on a Mandarin
lecture dataset demonstrate that the proposed method can significantly improve
the naturalness and expressiveness of the synthesized speech.
