# S-VoCAL: A Dataset and Evaluation Framework for Inferring Speaking Voice Character Attributes in Literature

arXiv: https://arxiv.org/abs/2603.00958 · HTML: https://mlsanity.com/paper/2603.00958/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 47 references that are in the map.
- First posted on arXiv 1 Mar 2026.

## Builds on (most cited first, 16 of 16)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [RULER: What's the Real Context Size of Your Long-Context Language Models?](https://mlsanity.com/paper/2404.06654/) (2024)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Text Embeddings by Weakly-Supervised Contrastive Pre-training](https://mlsanity.com/paper/2212.03533/) (2022)
- [How Contextual are Contextualized Word Representations? Comparing the Geometry of BERT, ELMo, and GPT-2 Embeddings](https://mlsanity.com/paper/1909.00512/) (2019)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Speak, Memory: An Archaeology of Books Known to ChatGPT/GPT-4](https://mlsanity.com/paper/2305.00118/) (2023)
- ["Let Your Characters Tell Their Story": A Dataset for Character-Centric Narrative Understanding](https://mlsanity.com/paper/2109.05438/) (2021)
- [Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works](https://mlsanity.com/paper/2404.12726/) (2024)
- [Ada-LEval: Evaluating long-context LLMs with length-adaptable benchmarks](https://mlsanity.com/paper/2404.06480/) (2024)
- [Heroes, Villains, and Victims, and GPT-3: Automated Extraction of Character Roles Without Training Data](https://mlsanity.com/paper/2205.07557/) (2022)
- [A Modular Approach for Multimodal Summarization of TV Shows](https://mlsanity.com/paper/2403.03823/) (2024)
- [The Emotion Dynamics of Literary Novels](https://mlsanity.com/paper/2403.02474/) (2024)
- [Improving Speech Prosody of Audiobook Text-to-Speech Synthesis with Acoustic and Textual Contexts](https://mlsanity.com/paper/2211.02336/) (2022)
- [MultiActor-Audiobook: Zero-Shot Audiobook Generation with Faces and Voices of Multiple Speakers](https://mlsanity.com/paper/2505.13082/) (2025)

## Built on by (most cited first, 1 of 1)

- [VidAudio-Bench: Benchmarking V2A and VT2A Generation across Four Audio Categories](https://mlsanity.com/paper/2604.10542/) (2026)

## Abstract (arXiv)

With recent advances in Text-to-Speech (TTS) systems, synthetic audiobook narration has seen increased interest, reaching unprecedented levels of naturalness. However, larger gaps remain in synthetic narration systems' ability to impersonate fictional characters, and convey complex emotions or prosody. A promising direction to enhance character identification is the assignment of plausible voices to each fictional characters in a book. This step typically requires complex inference of attributes in book-length contexts, such as a character's age, gender, origin or physical health, which in turns requires dedicated benchmark datasets to evaluate extraction systems' performances. We present S-VoCAL (Speaking Voice Character Attributes in Literature), the first dataset and evaluation framework dedicated to evaluate the inference of voice-related fictional character attributes. S-VoCAL entails 8 attributes grounded in sociophonetic studies, and 952 character-book pairs derived from Project Gutenberg. Its evaluation framework addresses the particularities of each attribute, and includes a novel similarity metric based on recent Large Language Models embeddings. We demonstrate the applicability of S-VoCAL by applying a simple Retrieval-Augmented Generation (RAG) pipeline to the task of inferring character attributes. Our results suggest that the RAG pipeline reliably infers attributes such as Age or Gender, but struggles on others such as Origin or Physical Health. The dataset and evaluation code are available at https://github.com/AbigailBerthe/S-VoCAL .