ToxicTone: A Mandarin Audio Dataset Annotated for Toxicity and Toxic Utterance Tonality

• URL: http://arxiv.org/abs/2505.15773v1
• Date: Wed, 21 May 2025 17:25:27 GMT
• Title: ToxicTone: A Mandarin Audio Dataset Annotated for Toxicity and Toxic Utterance Tonality
• Authors: Yu-Xiang Luo, Yi-Cheng Lin, Ming-To Chuang, Jia-Hung Chen, I-Ning Tsai, Pei Xing Kiew, Yueh-Hsuan Huang, Chien-Feng Liu, Yu-Chen Chen, Bo-Han Feng, Wenze Ren, Hung-yi Lee,
• Abstract summary: ToxicTone is the largest public dataset of its kind.<n>Our data is sourced from diverse real-world audio and organized into 13 topical categories.<n>We propose a multimodal detection framework that integrates acoustic, linguistic, and emotional features.
• Score: 35.517662288248225
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

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