# MuTox: Universal MUltilingual Audio-based TOXicity Dataset and Zero-shot Detector

arXiv: https://arxiv.org/abs/2401.05060 · HTML: https://mlsanity.com/paper/2401.05060/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 10 Jan 2024.

## Builds on (most cited first, 7 of 7)

- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [Seamless: Multilingual Expressive and Streaming Speech Translation](https://mlsanity.com/paper/2312.05187/) (2023)
- [Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models](https://mlsanity.com/paper/2206.09917/) (2022)
- [Toxicity in Multilingual Machine Translation at Scale](https://mlsanity.com/paper/2210.03070/) (2022)
- [DeToxy: A Large-Scale Multimodal Dataset for Toxicity Classification in Spoken Utterances](https://mlsanity.com/paper/2110.07592/) (2021)
- [ADIMA: Abuse Detection In Multilingual Audio](https://mlsanity.com/paper/2202.07991/) (2022)

## Built on by (most cited first, 15 of 15)

- [Spirit LM: Interleaved Spoken and Written Language Model](https://mlsanity.com/paper/2402.05755/) (2024)
- [Recent Advances in Hate Speech Moderation: Multimodality and the Role of Large Models](https://mlsanity.com/paper/2401.16727/) (2024)
- [BOUQuET: dataset, Benchmark and Open initiative for Universal Quality Evaluation in Translation](https://mlsanity.com/paper/2502.04314/) (2025)
- [Multilingual and Explainable Text Detoxification with Parallel Corpora](https://mlsanity.com/paper/2412.11691/) (2024)
- [Evaluating Bias in Spoken Dialogue LLMs for Real-World Decisions and Recommendations](https://mlsanity.com/paper/2510.02352/) (2025)
- [Enhancing Multilingual Voice Toxicity Detection with Speech-Text Alignment](https://mlsanity.com/paper/2406.10325/) (2024)
- [ToxicTone: A Mandarin Audio Dataset Annotated for Toxicity and Toxic Utterance Tonality](https://mlsanity.com/paper/2505.15773/) (2025)
- [On the Role of Speech Data in Reducing Toxicity Detection Bias](https://mlsanity.com/paper/2411.08135/) (2024)
- [GuardReasoner-Omni: A Reasoning-based Multi-modal Guardrail for Text, Image, Video, and Audio](https://mlsanity.com/paper/2602.03328/) (2026)
- [Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning](https://mlsanity.com/paper/2412.01408/) (2024)
- [MT-LENS: An all-in-one Toolkit for Better Machine Translation Evaluation](https://mlsanity.com/paper/2412.11615/) (2024)
- [ViToSA: Audio-Based Toxic Spans Detection on Vietnamese Speech Utterances](https://mlsanity.com/paper/2506.00636/) (2025)
- [Few-Shot Contrastive Adaptation for Audio Abuse Detection in Low-Resource Indic Languages](https://mlsanity.com/paper/2604.09094/) (2026)
- ["bot lane noob" Towards Deployment of NLP-based Toxicity Detectors in Video Games](https://mlsanity.com/paper/2604.10175/) (2026)
- [Forgive or forget: Understanding the context of hate in audio retrieval systems](https://mlsanity.com/paper/2606.05857/) (2026)

## Abstract (arXiv)

Research in toxicity detection in natural language processing for the speech modality (audio-based) is quite limited, particularly for languages other than English. To address these limitations and lay the groundwork for truly multilingual audio-based toxicity detection, we introduce MuTox, the first highly multilingual audio-based dataset with toxicity labels. The dataset comprises 20,000 audio utterances for English and Spanish, and 4,000 for the other 19 languages. To demonstrate the quality of this dataset, we trained the MuTox audio-based toxicity classifier, which enables zero-shot toxicity detection across a wide range of languages. This classifier outperforms existing text-based trainable classifiers by more than 1% AUC, while expanding the language coverage more than tenfold. When compared to a wordlist-based classifier that covers a similar number of languages, MuTox improves precision and recall by approximately 2.5 times. This significant improvement underscores the potential of MuTox in advancing the field of audio-based toxicity detection.