  Exploiting rich linguistic information in raw text is crucial for expressive
text-to-speech (TTS). As large scale pre-trained text representation develops,
bidirectional encoder representations from Transformers (BERT) has been proven
to embody semantic information and employed to TTS recently. However, original
or simply fine-tuned BERT embeddings still cannot provide sufficient semantic
knowledge that expressive TTS models should take into account. In this paper,
we propose a word-level semantic representation enhancing method based on
dependency structure and pre-trained BERT embedding. The BERT embedding of each
word is reprocessed considering its specific dependencies and related words in
the sentence, to generate more effective semantic representation for TTS. To
better utilize the dependency structure, relational gated graph network (RGGN)
is introduced to make semantic information flow and aggregate through the
dependency structure. The experimental results show that the proposed method
can further improve the naturalness and expressiveness of synthesized speeches
on both Mandarin and English datasets.
