# Text-aware and Context-aware Expressive Audiobook Speech Synthesis

arXiv: https://arxiv.org/abs/2406.05672 · HTML: https://mlsanity.com/paper/2406.05672/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2024.

## Built on by (most cited first, 3 of 3)

- [Sparse Autoencoders for Interpretable Emotion Control in Text-to-Speech](https://mlsanity.com/paper/2606.01479/) (2026)
- [Unlocking Fine-Grained and Within-Utterance Speaking Style Control in Prompt-Based Text-to-Speech Models](https://mlsanity.com/paper/2605.27376/) (2026)
- [Can Prosodic Style Be Inferred from Text Alone? Evidence from Unsupervised Acoustic Clusters](https://mlsanity.com/paper/2610.05575/) (2026)

## Abstract (arXiv)

Recent advances in text-to-speech have significantly improved the expressiveness of synthetic speech. However, a major challenge remains in generating speech that captures the diverse styles exhibited by professional narrators in audiobooks without relying on manually labeled data or reference speech. To address this problem, we propose a text-aware and context-aware(TACA) style modeling approach for expressive audiobook speech synthesis. We first establish a text-aware style space to cover diverse styles via contrastive learning with the supervision of the speech style. Meanwhile, we adopt a context encoder to incorporate cross-sentence information and the style embedding obtained from text. Finally, we introduce the context encoder to two typical TTS models, VITS-based TTS and language model-based TTS. Experimental results demonstrate that our proposed approach can effectively capture diverse styles and coherent prosody, and consequently improves naturalness and expressiveness in audiobook speech synthesis.