# VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining

arXiv: https://arxiv.org/abs/2302.12584 · HTML: https://mlsanity.com/paper/2302.12584/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SHAS: Approaching optimal Segmentation for End-to-End Speech Translation](https://mlsanity.com/paper/2202.04774/) (2022)
- [Transformer-Based Models for Automatic Identification of Argument Relations: A Cross-Domain Evaluation](https://mlsanity.com/paper/2011.13187/) (2020)
- [A Cascade Model for Argument Mining in Japanese Political Discussions: the QA Lab-PoliInfo-3 Case Study](https://mlsanity.com/paper/2207.01672/) (2022)

## Built on by (most cited first, 4 of 4)

- [Large Language Models in Argument Mining: A Survey](https://mlsanity.com/paper/2506.16383/) (2025)
- [OpenDebateEvidence: A Massive-Scale Argument Mining and Summarization Dataset](https://mlsanity.com/paper/2406.14657/) (2024)
- [DebateBench: A Challenging Long Context Reasoning Benchmark For Large Language Models](https://mlsanity.com/paper/2502.06279/) (2025)
- [A dataset of rated conceptual arguments](https://mlsanity.com/paper/2607.27499/) (2026)

## Abstract (arXiv)

In this paper, we describe VivesDebate-Speech, a corpus of spoken argumentation created to leverage audio features for argument mining tasks. The creation of this corpus represents an important contribution to the intersection of speech processing and argument mining communities, and one of the most complete publicly available resources in this topic. Moreover, we have performed a set of first-of-their-kind experiments which show an improvement when integrating audio features into the argument mining pipeline. The provided results can be used as a baseline for future research.