A Preliminary Case Study on Long-Form In-the-Wild Audio Spoofing Detection

TL;DR

This paper investigates the performance of audio spoofing detection systems in realistic, complex scenarios involving long-duration, multi-speaker audio with acoustic variations, highlighting current limitations and potential improvements.

Contribution

It provides the first analysis of spoofing detection in in-the-wild conditions with complex audio, revealing key issues and suggesting preliminary enhancements.

Findings

Current methods struggle with long, multi-speaker audio

Variations in duration and acoustics significantly affect detection performance

Identifies key challenges for real-world audio spoofing detection

Abstract

Audio spoofing detection has become increasingly important due to the rise in real-world cases. Current spoofing detectors, referred to as spoofing countermeasures (CM), are mainly trained and focused on audio waveforms with a single speaker and short duration. This study explores spoofing detection in more realistic scenarios, where the audio is long in duration and features multiple speakers and complex acoustic conditions. We test the widely-acquired AASIST under this challenging scenario, looking at the impact of multiple variations such as duration, speaker presence, and acoustic complexities on CM performance. Our work reveals key issues with current methods and suggests preliminary ways to improve them. We aim to make spoofing detection more applicable in more in-the-wild scenarios. This research is served as an important step towards developing detection systems that can handle the challenges of audio spoofing in real-world applications.