Expressive speech synthesis, like audiobook synthesis, is still challenging
for style representation learning and prediction. Deriving from reference audio
or predicting style tags from text requires a huge amount of labeled data,
which is costly to acquire and difficult to define and annotate accurately. In
this paper, we propose a novel framework for learning style representation from
abundant plain text in a self-supervised manner. It leverages an emotion
lexicon and uses contrastive learning and deep clustering. We further integrate
the style representation as a conditioned embedding in a multi-style
Transformer TTS. Comparing with multi-style TTS by predicting style tags
trained on the same dataset but with human annotations, our method achieves
improved results according to subjective evaluations on both in-domain and
out-of-domain test sets in audiobook speech. Moreover, with implicit
context-aware style representation, the emotion transition of synthesized audio
in a long paragraph appears more natural. The audio samples are available on
the demo web.

本文提出了一种利用自我监督学习从大量纯文本中学习风格表达的新框架，采用情感词典和对比学习和深度聚类，最终在多风格变换文本到语音系统中得到了更好的结果。