  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.

Sound;Computation and Language;Audio and Speech Processing