  In this paper, we focus on audio violence detection (AVD). AVD is necessary
for several reasons, especially in the context of maintaining safety,
preventing harm, and ensuring security in various environments. This calls for
accurate AVD systems. Like many related applications in audio processing, the
most common approach for improving the performance, would be by leveraging
self-supervised (SSL) pre-trained models (PTMs). However, as these SSL models
are very large models with million of parameters and this can hinder real-world
deployment especially in compute-constraint environment. To resolve this, we
propose the usage of speaker recognition models which are much smaller compared
to the SSL models. Experimentation with speaker recognition model embeddings
with SVM & Random Forest as classifiers, we show that speaker recognition model
embeddings perform the best in comparison to state-of-the-art (SOTA) SSL models
and achieve SOTA results.
