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Proceedings of the AAAI Conference on Artificial Intelligence

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Beyond Content: A Comprehensive Speech Toxicity Dataset a...
2026-03-14 · via Proceedings of the AAAI Conference on Artificial Intelligence

Authors

  • Zhongjie Ba The State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, Zhejiang, China
  • Liang Yi The State Key Laboratory of Blockchain and Data Security, Zhejiang University
  • Peng Cheng The State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, Zhejiang, China
  • Qingcao Li School of Cyber Science and Engineering, Nanjing University of Science and Technology The State Key Laboratory of Blockchain and Data Security, Zhejiang University
  • Qinglong Wang The State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, Zhejiang, China
  • Li Lu The State Key Laboratory of Blockchain and Data Security, Zhejiang University Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security, Hangzhou, Zhejiang, China

DOI:

https://doi.org/10.1609/aaai.v40i1.36960

Abstract

Toxic speech detection has become a crucial challenge in maintaining safe online communication environments. However, existing approaches to toxic speech detection often neglect the contribution of paralinguistic cues, such as emotion, intonation, and speech rate, which are key to detecting speech toxicity. Moreover, current toxic speech datasets are predominantly text-based, limiting the development of models that can capture paralinguistic cues. To address these challenges, we present ToxiAlert-Bench, a large-scale audio dataset comprising over 30,000 audio clips annotated with seven major toxic categories and twenty fine-grained toxic labels. Uniquely, our dataset annotates toxicity sources—distinguishing between textual content and paralinguistic origins—for comprehensive toxic speech analysis. Furthermore, we propose a dual-head neural network with a multi-stage training strategy tailored for toxic speech detection. This architecture features two task-specific classification headers: one for identifying the source of sensitivity (textual or paralinguistic), and the other for categorizing the specific toxic type. The training process involves independent head training followed by joint fine-tuning to reduce task interference. To mitigate data class imbalance, we incorporate class-balanced sampling and weighted loss functions. Our experimental results show that leveraging paralinguistic features significantly improves detection performance. Our method consistently outperforms existing baselines across multiple evaluation metrics, with a 21.1% relative improvement in Macro-F1 score and a 13.0% relative gain in accuracy over the strongest baseline, highlighting its enhanced effectiveness and practical applicability.

How to Cite

Ba, Z., Yi, L., Cheng, P., Li, Q., Wang, Q., & Lu, L. (2026). Beyond Content: A Comprehensive Speech Toxicity Dataset and Detection Framework Incorporating Paralinguistic Cues. Proceedings of the AAAI Conference on Artificial Intelligence, 40(1), 21–29. https://doi.org/10.1609/aaai.v40i1.36960

Issue

Section

AAAI Technical Track on Application Domains I

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