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

Autor:	Liu, Xuechen, Wang, Xin, Yamagishi, Junichi
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Cryptography and Security Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to the 23rd International Conference of the Biometrics Special Interest Group (BIOSIG 2024). Copyright might be transferred, in such case the current version may be replaced
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.14066 Zobrazit plný text záznamu View this record from Arxiv