# Voice Communication Analysis in Esports

arXiv: https://arxiv.org/abs/2411.19793 · HTML: https://mlsanity.com/paper/2411.19793/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 19 references that are in the map.
- First posted on arXiv 21 Nov 2024.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [MTEB: Massive Text Embedding Benchmark](https://mlsanity.com/paper/2210.07316/) (2022)
- [DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games](https://mlsanity.com/paper/2204.12750/) (2022)
- [League of Legends: Real-Time Result Prediction](https://mlsanity.com/paper/2309.02449/) (2023)
- [Individualized Context-Aware Tensor Factorization for Online Games Predictions](https://mlsanity.com/paper/2102.11352/) (2021)

## Abstract (arXiv)

In most team-based esports, voice communications are prominent in the team efficiency and synergy. In fact it has been observed that not only the skill aspect of the team but also the team effective voice communication comes into play when trying to have good performance in official matches. With the recent emergence of LLM (Large Language Models) tools regarding NLP (Natural Language Processing) (Vaswani et. al.), we decided to try applying them in order to have a better understanding on how to improve the effectiveness of the voice communications. In this paper the study has been made through the prism of League of Legends esport. However the main concepts and ideas can be easily applicable in any other team related esports.