Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: USTC-KXDIGIT System Description for ASVspoof5 Challenge
summary: This paper describes the USTC-KXDIGIT system submitted to the ASVspoof5
Challenge for Track 1 (speech deepfake detection) and Track 2 (spoofing-robust
automatic speaker verification, SASV). Track 1 showcases a diverse range of
technical qualities from potential processing algorithms and includes both open
and closed conditions. For these conditions, our system consists of a cascade
of a frontend feature extractor and a back-end classifier. We focus on
extensive embedding engineering and enhancing the generalization of the
back-end classifier model. Specifically, the embedding engineering is based on
hand-crafted features and speech representations from a self-supervised model,
used for closed and open conditions, respectively. To detect spoof attacks
under various adversarial conditions, we trained multiple systems on an
augmented training set. Additionally, we used voice conversion technology to
synthesize fake audio from genuine audio in the training set to enrich the
synthesis algorithms. To leverage the complementary information learned by
different model architectures, we employed activation ensemble and fused scores
from different systems to obtain the final decision score for spoof detection.
During the evaluation phase, the proposed methods achieved 0.3948 minDCF and
14.33% EER in the close condition, and 0.0750 minDCF and 2.59% EER in the open
condition, demonstrating the robustness of our submitted systems under
adversarial conditions. In Track 2, we continued using the CM system from Track
1 and fused it with a CNN-based ASV system. This approach achieved 0.2814
min-aDCF in the closed condition and 0.0756 min-aDCF in the open condition,
showcasing superior performance in the SASV system.
Please check whether this paper is about 'Voice Conversion' or not.
article info.
title: USTC-KXDIGIT System Description for ASVspoof5 Challenge
summary: This paper describes the USTC-KXDIGIT system submitted to the ASVspoof5 Challenge for Track 1 (speech deepfake detection) and Track 2 (spoofing-robust automatic speaker verification, SASV). Track 1 showcases a diverse range of technical qualities from potential processing algorithms and includes both open and closed conditions. For these conditions, our system consists of a cascade of a frontend feature extractor and a back-end classifier. We focus on extensive embedding engineering and enhancing the generalization of the back-end classifier model. Specifically, the embedding engineering is based on hand-crafted features and speech representations from a self-supervised model, used for closed and open conditions, respectively. To detect spoof attacks under various adversarial conditions, we trained multiple systems on an augmented training set. Additionally, we used voice conversion technology to synthesize fake audio from genuine audio in the training set to enrich the synthesis algorithms. To leverage the complementary information learned by different model architectures, we employed activation ensemble and fused scores from different systems to obtain the final decision score for spoof detection. During the evaluation phase, the proposed methods achieved 0.3948 minDCF and 14.33% EER in the close condition, and 0.0750 minDCF and 2.59% EER in the open condition, demonstrating the robustness of our submitted systems under adversarial conditions. In Track 2, we continued using the CM system from Track 1 and fused it with a CNN-based ASV system. This approach achieved 0.2814 min-aDCF in the closed condition and 0.0756 min-aDCF in the open condition, showcasing superior performance in the SASV system.
id: http://arxiv.org/abs/2409.01695v1
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