Investigating the Potential of Multi-Stage Score Fusion in Spoofing-Aware Speaker Verification
Küçük Resim Yok
Tarih
2025
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Ieee
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Despite improvements in automatic speaker verification (ASV), vulnerability against spoofing attacks remains a major concern. In this study, we investigate the integration of ASV and countermeasure (CM) subsystems into a modular spoof-aware speaker verification (SASV) framework. Unlike conventional single-stage score-level fusion methods, we explore the potential of a multi-stage approach that utilizes the ASV and CM systems in multiple stages. By leveraging ECAPA-TDNN (ASV) and AASIST (CM) subsystems, we consider support vector machine and logistic regression classifiers to achieve SASV. In the second stage, we integrate their outputs with the original score to revise fusion back-end classifiers. Additionally, we incorporate another auxiliary score from RawGAT (CM) to further enhance our SASV framework. Our approach yields an equal error rate (EER) of 1.30% on the evaluation dataset of the SASV2022 challenge, representing a 24% relative improvement over the baseline system.
Açıklama
33rd Conference on Signal Processing and Communications Applications-SIU-Annual -- JUN 25-28, 2025 -- Istanbul, TURKIYE
Anahtar Kelimeler
speaker verification, spoofing countermeasure, spoof-aware speaker verification
Kaynak
2025 33Rd Signal Processing and Communications Applications Conference, Siu
WoS Q Değeri
N/A
Scopus Q Değeri
N/A
