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.

通过创造多语言音频数据集MuTox，实现了跨多种语言的零射击毒性检测，相比现有的基于文本的训练分类器，MuTox的AUC值提高了超过1％，语言覆盖范围扩大了10倍以上，相比于基于词汇列表的分类器，MuTox的精确度和召回率提高了约2.5倍，这一显著改进突显出MuTox在音频毒性检测领域的潜力。

MuTox：通用多语种基于音频的毒性数据集和零样本检测器