Self-supervised Context-aware Style Representation for Expressive Speech Synthesis

Understand

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.