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：语音模态（基于音频）的自然语言处理毒性检测研究相当有限，尤其对于英语以外的语言。为应对这些局限并为真正多语言的音频毒性检测奠定基础，我们推出了MuTox——首个带有毒性标签的高度多语言音频数据集。该数据集包含20,000条英语和西班牙语音频话语，以及其余19种语言各4,000条。为验证数据集质量，我们训练了MuTox音频毒性分类器，该模型能实现跨多种语言的零样本毒性检测。该分类器在AUC指标上超越现有基于文本的可训练分类器1%以上，同时将语言覆盖范围扩展十倍以上。与覆盖语言数量相近的基于词表的分类器相比，MuTox将精确率和召回率提升约2.5倍。这一显著改进彰显了MuTox在推进音频毒性检测领域发展的潜力。