Introduces the Indic-CodecFake dataset for Indic codec deepfakes and SATYAM, a novel hyperbolic ALM that outperforms baselines through dual-stage semantic-prosodic fusion using Bhattacharya distance.
arXiv preprint arXiv:2506.12627 , year=
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HCFD is a new pathology-aware benchmark and dataset for codec-fake audio detection in healthcare, with PHOENIX-Mamba achieving up to 97% accuracy by modeling fakes as modes in hyperbolic space.
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.
citing papers explorer
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Indic-CodecFake meets SATYAM: Towards Detecting Neural Audio Codec Synthesized Speech Deepfakes in Indic Languages
Introduces the Indic-CodecFake dataset for Indic codec deepfakes and SATYAM, a novel hyperbolic ALM that outperforms baselines through dual-stage semantic-prosodic fusion using Bhattacharya distance.
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HCFD: A Benchmark for Audio Deepfake Detection in Healthcare
HCFD is a new pathology-aware benchmark and dataset for codec-fake audio detection in healthcare, with PHOENIX-Mamba achieving up to 97% accuracy by modeling fakes as modes in hyperbolic space.
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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.