@inproceedings{michel-etal-2026-computational,
    title = "Computational Narrative Understanding for Expressive Text-to-Speech",
    author = "Michel, Gaspard  and
      Epure, Elena V.  and
      Cerisara, Christophe",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.308/",
    pages = "6194--6215",
    ISBN = "979-8-89176-395-1",
    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., ``\textit{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-\textit{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 \url{https://libriquote.github.io/}."
}