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.

翻译：毒性语音检测已成为维护在线交流环境安全的重要挑战。然而，现有毒性语音检测方法往往忽视情绪、语调、语速等副语言线索的贡献，而这些正是检测语音毒性的关键要素。此外，当前毒性语音数据集主要基于文本，限制了能够捕捉副语言线索的模型发展。为应对这些挑战，我们提出了ToxiAlert-Bench——一个大规模音频数据集，包含超过3万个音频片段，标注了七种主要毒性类别和二十种细粒度毒性标签。独特之处在于，本数据集标注了毒性来源——区分文本内容和副语言起源——以实现全面的毒性语音分析。进一步地，我们提出了一种专为毒性语音检测设计的双头神经网络与多阶段训练策略。该架构包含两个任务特定分类头：一个用于识别敏感来源（文本或副语言），另一个用于分类具体毒性类型。训练过程采用独立头训练后联合微调的方法，以减少任务干扰。为缓解数据类别不平衡问题，我们引入了类别平衡采样和加权损失函数。实验结果表明，利用副语言特征显著提升了检测性能。本方法在多项评估指标上持续超越现有基线，相较于最强基线，Macro-F1分数相对提升21.1%，准确率相对提高13.0%，凸显了其增强的有效性和实际应用能力。