Global anchoring outperforms pairwise verification in synthetic speech source tracing by preserving more discriminative embedding directions, yielding lower error rates on in-domain and out-of-domain data.
The dawn of a text-dependent society: deepfakes as a threat to speech verification systems,
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SpAArSIST sparsifies AASIST by swapping learned pooling for explicit magnitude-based scoring and mean aggregation, cutting compute 20.7% and improving In-the-Wild EER to 2.82%.
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The Hidden Cost of Pairwise Verification in Synthetic Speech Source Tracing
Global anchoring outperforms pairwise verification in synthetic speech source tracing by preserving more discriminative embedding directions, yielding lower error rates on in-domain and out-of-domain data.
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SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing
SpAArSIST sparsifies AASIST by swapping learned pooling for explicit magnitude-based scoring and mean aggregation, cutting compute 20.7% and improving In-the-Wild EER to 2.82%.