Towards Cross-Lingual Audio Abuse Detection in Low-Resource Settings with Few-Shot Learning

Summary

This paper explores how to detect abusive language in audio recordings of low-resource languages using a method called Few-Shot Learning (FSL), which requires only a small amount of training data.

What's the problem?

Detecting abusive content in audio is challenging, especially in languages that don't have a lot of available data or resources. Most existing systems struggle to identify abusive language accurately because they are trained on limited examples, making it hard for them to recognize different ways people might express abuse in various languages.

What's the solution?

The researchers propose a new approach that uses pre-trained audio models like Wav2Vec and Whisper to help detect abusive language across multiple languages. They employ a technique called Model-Agnostic Meta-Learning (MAML) within the Few-Shot Learning framework, allowing the system to learn from just a few examples of abusive speech. By training the model on a dataset called ADIMA, they show that it can effectively generalize and identify abusive language even when there is little data available for each specific language.

Why it matters?

This research is significant because it helps improve the ability to detect harmful speech in languages that are often overlooked. By using advanced machine learning techniques, the study aims to make online spaces safer for speakers of low-resource languages, ensuring that everyone can communicate without facing abuse or harassment.