  Despite prosody is related to the linguistic information up to the discourse
structure, most text-to-speech (TTS) systems only take into account that within
each sentence, which makes it challenging when converting a paragraph of texts
into natural and expressive speech. In this paper, we propose to use the text
embeddings of the neighboring sentences to improve the prosody generation for
each utterance of a paragraph in an end-to-end fashion without using any
explicit prosody features. More specifically, cross-utterance (CU) context
vectors, which are produced by an additional CU encoder based on the sentence
embeddings extracted by a pre-trained BERT model, are used to augment the input
of the Tacotron2 decoder. Two types of BERT embeddings are investigated, which
leads to the use of different CU encoder structures. Experimental results on a
Mandarin audiobook dataset and the LJ-Speech English audiobook dataset
demonstrate the use of CU information can improve the naturalness and
expressiveness of the synthesized speech. Subjective listening testing shows
most of the participants prefer the voice generated using the CU encoder over
that generated using standard Tacotron2. It is also found that the prosody can
be controlled indirectly by changing the neighbouring sentences.
