  With the advent of deep learning, a huge number of text-to-speech (TTS)
models which produce human-like speech have emerged. Recently, by introducing
syntactic and semantic information w.r.t the input text, various approaches
have been proposed to enrich the naturalness and expressiveness of TTS models.
Although these strategies showed impressive results, they still have some
limitations in utilizing language information. First, most approaches only use
graph networks to utilize syntactic and semantic information without
considering linguistic features. Second, most previous works do not explicitly
consider adjacent words when encoding syntactic and semantic information, even
though it is obvious that adjacent words are usually meaningful when encoding
the current word. To address these issues, we propose Relation-aware Word
Encoding Network (RWEN), which effectively allows syntactic and semantic
information based on two modules (i.e., Semantic-level Relation Encoding and
Adjacent Word Relation Encoding). Experimental results show substantial
improvements compared to previous works.
