  Recent advances in text-to-speech have significantly improved the
expressiveness of synthesized speech. However, it is still challenging to
generate speech with contextually appropriate and coherent speaking style for
multi-sentence text in audiobooks. In this paper, we propose a context-aware
coherent speaking style prediction method for audiobook speech synthesis. To
predict the style embedding of the current utterance, a hierarchical
transformer-based context-aware style predictor with a mixture attention mask
is designed, considering both text-side context information and speech-side
style information of previous speeches. Based on this, we can generate
long-form speech with coherent style and prosody sentence by sentence.
Objective and subjective evaluations on a Mandarin audiobook dataset
demonstrate that our proposed model can generate speech with more expressive
and coherent speaking style than baselines, for both single-sentence and
multi-sentence test.
