  Humans often speak in a continuous manner which leads to coherent and
consistent prosody properties across neighboring utterances. However, most
state-of-the-art speech synthesis systems only consider the information within
each sentence and ignore the contextual semantic and acoustic features. This
makes it inadequate to generate high-quality paragraph-level speech which
requires high expressiveness and naturalness. To synthesize natural and
expressive speech for a paragraph, a context-aware speech synthesis system
named MaskedSpeech is proposed in this paper, which considers both contextual
semantic and acoustic features. Inspired by the masking strategy in the speech
editing research, the acoustic features of the current sentence are masked out
and concatenated with those of contextual speech, and further used as
additional model input. The phoneme encoder takes the concatenated phoneme
sequence from neighboring sentences as input and learns fine-grained semantic
information from contextual text. Furthermore, cross-utterance coarse-grained
semantic features are employed to improve the prosody generation. The model is
trained to reconstruct the masked acoustic features with the augmentation of
both the contextual semantic and acoustic features. Experimental results
demonstrate that the proposed MaskedSpeech outperformed the baseline system
significantly in terms of naturalness and expressiveness.
