ToxicTone: A Mandarin Audio Dataset Annotated for Toxicity and Toxic Utterance Tonality
Proc. Interspeech 2025 , 4008–4012 , 2025
Abstract
Despite extensive research on toxic speech detection in text, a critical gap remains in handling spoken Mandarin audio. The lack of annotated datasets that capture the unique prosodic cues and culturally specific expressions in Mandarin leaves spoken toxicity underexplored. To address this, we introduce ToxicTone – the largest public dataset of its kind – featuring detailed annotations that distinguish both forms of toxicity (e.g., profanity, bullying) and sources of toxicity (e.g., anger, sarcasm, dismissiveness). Our data, sourced from diverse real-world audio and organized into 13 topical categories, mirrors authentic communication scenarios. We also propose a multimodal detection framework that integrates acoustic, linguistic, and emotional features using state-of-the-art speech and emotion encoders. Extensive experiments show our approach outperforms text-only and baseline models, underscoring the essential role of speech-specific cues in revealing hidden toxic expressions.
BibTeX
@inproceedings{luo2025toxictone,
title = {ToxicTone: A Mandarin Audio Dataset Annotated for Toxicity and Toxic Utterance Tonality},
author = {Luo, Yu-Xiang and Lin, Yi-Cheng and Chuang, Ming-To and Chen, Jia-Hung and Tsai, I-Ning and Kiew, Pei Xing and Huang, Yueh-Hsuan and Liu, Chien- Feng and Chen, Yu-Chen and Feng, Bo-Han and Ren, Wenze and Lee, Hung-yi},
booktitle = {Proc. Interspeech 2025},
year = {2025},
pages = {4008--4012},
doi = {10.21437/Interspeech.2025-679},
}