A Chapter-Wise Understanding System for Text-To-Speech in Chinese Novels

In TTS-based audiobook production, multi-role dubbing and emotional expressions can significantly improve the naturalness of audiobooks. However, it requires manual annotation of original novels with explicit speaker and emotion tags in sentence level, which is extremely time-consuming and costly. In this paper, we propose a chapter-wise understanding system for Chinese novels, to predict speaker and emotion tags automatically based on the chapter-level context. Compared with baselines of each component, our models obtain higher performance. Audiobooks produced by our proposed system along with a multi-speaker emotional TTS system, are proved to achieve comparable quality score to audiobooks made by individual producers. Demos are demonstrated in https://jeffpan.net/icassp/2021/main.html.