  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.
