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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.14959 |
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Table of Contents:
- In this paper, we introduce the discrete optimal transport voice conversion ($k$DOT-VC) method. Comparison with $k$NN-VC, SinkVC, and Gaussian optimal transport (MKL) demonstrates stronger domain adaptation abilities of our method. We use the probabilistic nature of optimal transport (OT) and show that $k$DOT-VC is an effective black-box adversarial attack against modern audio anti-spoofing countermeasures (CMs). Our attack operates as a post-processing, distribution-alignment step: frame-level {WavLM} embeddings of generated speech are aligned to an unpaired bona fide pool via entropic OT and a top-$k$ barycentric projection, then decoded with a neural vocoder. Ablation analysis indicates that distribution-level alignment is a powerful and stable attack for deployed CMs.