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.

翻译：在自然语言处理领域中，针对语音模态（音频）的毒性检测研究相当有限，尤其对于英语以外的语种。为解决这一局限性并奠定真正多语言音频毒性检测的基础，我们提出MuTox——首个具备毒性标注的高覆盖多语言音频数据集。该数据集包含英语和西班牙语的20,000条音频话语，以及其他19种语言各4,000条。为验证该数据集质量，我们训练了MuTox音频毒性分类器，该分类器支持跨广泛语种的零样本毒性检测。相较现有基于文本的可训练分类器，该分类器的AUC值提升超过1%，同时将语言覆盖范围扩大十倍以上。与覆盖语种数量相当的基于词表的分类器相比，MuTox在精确率和召回率上均实现约2.5倍的提升。这一显著进步彰显了MuTox在推动音频毒性检测领域发展方面的潜力。