On the potential of jointly-optimised solutions to spoofing attack detection and automatic speaker verification
원문 초록
The spoofing-aware speaker verification (SASV) challenge was designed to promote the study of jointly-optimised solutions to accomplish the traditionally separately-optimised tasks of spoofing detection and speaker verification. Jointly-optimised systems have the potential to operate in synergy as a better performing solution to the single task of reliable speaker verification. However, none of the 23 submissions to SASV 2022 are jointly optimised. We have hence sought to determine why separately-optimised sub-systems perform best or why joint optimisation was not successful. Experiments reported in this paper show that joint optimisation is successful in improving robustness to spoofing but that it degrades speaker verification performance. The findings suggest that spoofing detection and speaker verification sub-systems should be optimised jointly in a manner which reflects the differences in how information provided by each sub-system is complementary to that provided by the other. Progress will also likely depend upon the collection of data from a larger number of speakers.
함께 살펴볼 논문
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Pushing the Performance of Synthetic Speech Detection with Kolmogorov-Arnold Networks and Self-Supervised Learning Models
ASVspoof2019 vs. ASVspoof5: Assessment and Comparison
Pushing the Frontiers of Self-Distillation Prototypes Network with Dimension Regularization and Score Normalization
SSPS: Self-Supervised Positive Sampling for Robust Self-Supervised Speaker Verification
CBW: Towards Dataset Ownership Verification for Speaker Verification via Clustering-based Backdoor Watermarking
Self-Supervised Frameworks for Speaker Verification via Bootstrapped Positive Sampling
토론
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