Online abusive content detection, particularly in low-resource settings and within the audio modality, remains underexplored. We investigate the potential of pre-trained audio representations for detecting abusive language in low-resource languages, in this case, in Indian languages using Few Shot Learning (FSL). Leveraging powerful representations from models such as Wav2Vec and Whisper, we explore cross-lingual abuse detection using the ADIMA dataset with FSL. Our approach integrates these representations within the Model-Agnostic Meta-Learning (MAML) framework to classify abusive language in 10 languages. We experiment with various shot sizes (50-200) evaluating the impact of limited data on performance. Additionally, a feature visualization study was conducted to better understand model behaviour. This study highlights the generalization ability of pre-trained models in low-resource scenarios and offers valuable insights into detecting abusive language in multilingual contexts.

翻译：在线辱骂内容检测，特别是在低资源场景和音频模态中，仍处于探索不足的状态。本研究探讨了预训练音频表征在低资源语言（本文以印度语言为例）中检测辱骂语言的潜力，并采用少样本学习（FSL）方法。我们利用Wav2Vec和Whisper等模型生成的强大表征，基于ADIMA数据集开展跨语言辱骂检测的FSL研究。该方法将这些表征集成到模型无关元学习（MAML）框架中，实现了对10种语言中辱骂语言的分类。我们通过不同样本量（50-200）的实验评估了有限数据对性能的影响。此外，还进行了特征可视化研究以深入理解模型行为。本研究凸显了预训练模型在低资源场景下的泛化能力，并为多语言环境中的辱骂语言检测提供了重要见解。