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    <abstract>Recent advances in text-to-speech (TTS) have been driven by large, multi-domain speech corpora, yet the expressive potential of audiobook data remains underexamined. We argue that human-narrated audiobooks, particularly fictional works, contain rich and diverse prosodic cues arising from the natural alternation between neutral narration and expressive character dialogue. Building from this observation, we introduce LibriQuote, a large-scale 5.3K hours of expressive speech drawn from character quotations.Each quote is supplemented with contextual pseudo-labels for speech verbs and adverbs that characterize the intended delivery of direct speech (e.g., “he whispered softly”).We found that fine-tuning a flow-matching model on LibriQuote yields substantial improvements in expressivity and intelligibility, while training from scratch enhances expressiveness of an autoregressive TTS model.Benchmarking on LibriQuote-test highlights significant variability across systems in generating expressive speech.We publicly release the dataset, code, and evaluation resources to facilitate reproducibility.Audio samples can be found at https://libriquote.github.io/.</abstract>
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%0 Conference Proceedings
%T Computational Narrative Understanding for Expressive Text-to-Speech
%A Michel, Gaspard
%A Epure, Elena V.
%A Cerisara, Christophe
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F michel-etal-2026-computational
%X Recent advances in text-to-speech (TTS) have been driven by large, multi-domain speech corpora, yet the expressive potential of audiobook data remains underexamined. We argue that human-narrated audiobooks, particularly fictional works, contain rich and diverse prosodic cues arising from the natural alternation between neutral narration and expressive character dialogue. Building from this observation, we introduce LibriQuote, a large-scale 5.3K hours of expressive speech drawn from character quotations.Each quote is supplemented with contextual pseudo-labels for speech verbs and adverbs that characterize the intended delivery of direct speech (e.g., “he whispered softly”).We found that fine-tuning a flow-matching model on LibriQuote yields substantial improvements in expressivity and intelligibility, while training from scratch enhances expressiveness of an autoregressive TTS model.Benchmarking on LibriQuote-test highlights significant variability across systems in generating expressive speech.We publicly release the dataset, code, and evaluation resources to facilitate reproducibility.Audio samples can be found at https://libriquote.github.io/.
%U https://aclanthology.org/2026.findings-acl.308/
%P 6194-6215

Markdown (Informal)

[Computational Narrative Understanding for Expressive Text-to-Speech](https://aclanthology.org/2026.findings-acl.308/) (Michel et al., Findings 2026)

• Computational Narrative Understanding for Expressive Text-to-Speech (Michel et al., Findings 2026)

ACL

• Gaspard Michel, Elena V. Epure, and Christophe Cerisara. 2026. Computational Narrative Understanding for Expressive Text-to-Speech. In Findings of the Association for Computational Linguistics: ACL 2026, pages 6194–6215, San Diego, California, United States. Association for Computational Linguistics.