Beyond Content: A Comprehensive Speech Toxicity Dataset and Detection Framework Incorporating Paralinguistic Cues

2026-05-15

AI Summary

This study addresses the limitations of existing speech toxicity detection methods, which predominantly rely on textual content while neglecting paralinguistic cues such as emotion and prosody, and suffer from a scarcity of large-scale annotated data. To bridge this gap, the authors introduce ToxiAlert-Bench, a novel dataset comprising over 30,000 audio clips, systematically annotated with seven coarse-grained toxicity categories and twenty fine-grained labels, explicitly distinguishing toxicity sources—textual versus paralinguistic. They propose a source-aware dual-task detection framework employing a two-headed neural network and a multi-stage training strategy (independent pretraining followed by joint fine-tuning), enhanced with class-balanced sampling and a weighted loss function to effectively disentangle textual and paralinguistic toxicity signals. Experiments demonstrate that the proposed approach achieves a relative improvement of 21.1% in Macro-F1 and 13.0% in accuracy, significantly outperforming current baselines.

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.

Problem

• toxic speech detection
• paralinguistic cues
• audio dataset
• content toxicity
• speech toxicity

Innovation

• paralinguistic cues
• toxic speech detection
• dual-head neural network
• multi-stage training
• ToxiAlert-Bench