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.

本研究提出了 Relation-aware Word Encoding Network（RWEN）模型，通过两个模块（即语义级别关系编码和相邻单词关系编码）有效利用语法和语义信息，与以前的方法相比，实验结果显示出了显著的改进。