# Generalizable Audio Spoofing Detection using Non-Semantic Representations

## TL;DR

This paper introduces a new audio spoofing detection method using non-semantic features, which significantly improves generalization to real-world data and outperforms existing approaches in diverse settings.

## Contribution

The study proposes leveraging non-semantic universal audio representations, specifically TRILL and TRILLsson, to enhance the robustness and generalizability of spoofing detection methods.

## Key findings

- Achieves comparable in-domain performance
- Outperforms state-of-the-art out-of-domain detection
- Demonstrates superior generalization on public data

## Abstract

Rapid advancements in generative modeling have made synthetic audio generation easy, making speech-based services vulnerable to spoofing attacks. Consequently, there is a dire need for robust countermeasures more than ever. Existing solutions for deepfake detection are often criticized for lacking generalizability and fail drastically when applied to real-world data. This study proposes a novel method for generalizable spoofing detection leveraging non-semantic universal audio representations. Extensive experiments have been performed to find suitable non-semantic features using TRILL and TRILLsson models. The results indicate that the proposed method achieves comparable performance on the in-domain test set while significantly outperforming state-of-the-art approaches on out-of-domain test sets. Notably, it demonstrates superior generalization on public-domain data, surpassing methods based on hand-crafted features, semantic embeddings, and end-to-end architectures.