  In this study, we investigate representations from paralingual Pre-Trained
model (PTM) for Audio Abuse Detection (AAD), which has not been explored for
AAD. Our results demonstrate their superiority compared to other PTM
representations on the ADIMA benchmark. Furthermore, combining PTM
representations enhances AAD performance. Despite these improvements,
challenges with cross-lingual generalizability still remain, and certain
languages require training in the same language. This demands individual models
for different languages, leading to scalability, maintenance, and resource
allocation issues and hindering the practical deployment of AAD systems in
linguistically diverse real-world environments. To address this, we introduce
CoLLAB, a novel framework that doesn't require training and allows seamless
merging of models trained in different languages through weight-averaging. This
results in a unified model with competitive AAD performance across multiple
languages.
