---
title: "Computational Narrative Understanding for Expressive"
description: "They build LibriQuote from English fiction recordings on LibriVox, matched with Project Gutenberg texts. 54 references."
authors: ["Gaspard Michel", "Elena V. Epure", "Christophe Cerisara"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.308/"
doi: "10.18653/v1/2026.findings-acl.308"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/michel-2026-computational-narrative-understanding-expressive-text/"
---
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# Computational Narrative Understanding for Expressive Text-to-Speech

**Authors:** [Gaspard Michel](https://md.research.elamur.ai/free/en/authors/gaspard-michel/), [Elena V. Epure](https://md.research.elamur.ai/free/en/authors/elena-v-epure/) and [Christophe Cerisara](https://md.research.elamur.ai/free/en/authors/christophe-cerisara/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-acl.308/) · [PDF](https://aclanthology.org/2026.findings-acl.308.pdf) · [DOI 10.18653/v1/2026.findings-acl.308](https://doi.org/10.18653/v1/2026.findings-acl.308) · [OpenAlex](https://openalex.org/W7166837591)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Audiobook corpora are often seen as too flat for training expressive text-to-speech (TTS). The authors challenge this view. They note that fiction recordings alternate between neutral narration and livelier character dialogue, and existing segmentations blend the two. On LibriHeavy-small, pitch variability increases as a segment contains more quotations.

They build LibriQuote from English fiction recordings on LibriVox, matched with Project Gutenberg texts. Quotations are detected with BookNLP, the audio is transcribed by a Zipformer-Transducer ASR model, and text and audio are then aligned. Every quote also gets automatically extracted speech verbs and adverbs (e.g., "whispered softly") as pseudo-labels of intended delivery, and humans validated these labels.

Fine-tuning the flow-matching F5-TTS on the quotations improves both expressivity and intelligibility. For the autoregressive SparkTTS, fine-tuning mainly improves intelligibility, while training from scratch increases expressivity but lowers intelligibility. Benchmarking shows wide gaps between systems, and IndexTTS2 performs best. The resource supports expressive TTS research and Digital Humanities studies of audiobook prosody.

## Key points
- LibriQuote-train contains about 5.3K hours (5,359 h) of character quotations and 12.7K hours of narration from roughly 3,300 speakers. LibriQuote-test has 7.4 hours of quotations from 15 speakers.
- The corpus covers about 3 million quotations from 2,991 public-domain novels. It ships with paragraph and quote alignments, so users can extract book context of any length.
- In an analysis of 120,000 LibriHeavy-small segments, 75% contained narration only. Pitch standard deviation correlated with the number of quotes per segment (Spearman ρ = 0.218, p \< 0.001).
- Expressivity was assessed with an LALM-as-a-judge and a human listening study, and the two showed closely aligned trends. IndexTTS2 reached ground-truth expressivity but sometimes conveyed unintended emotions.
- The dataset is released on Huggingface (gasmichel/LibriQuote) and GitHub (deezer/libriquote). The authors note it has no WER-based filtering yet.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Gaspard Michel, Elena V. Epure and Christophe Cerisara. 2026. Computational Narrative Understanding for Expressive Text-to-Speech. Findings of the Association for Computational Linguistics: ACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{michel2026computational,
  title = {{Computational Narrative Understanding for Expressive Text-to-Speech}},
  author = {Gaspard Michel, Elena V. Epure and Christophe Cerisara},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.308},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.308}
}
```

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*Updated 1 November 2026*