TD-VIM creates signal-level morphed voice samples that achieve G-MAP attack success rates up to 99.74% against deep-learning and commercial speaker verification systems.
ArXiv abs/2004.00526
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GaborNet replaces sinc functions with Gabor filters in raw-audio neural networks and is tested for audio spoof detection with augmentations in RawNet2 and RawGAT-ST.
citing papers explorer
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Time-Domain Voice Identity Morphing (TD-VIM): A Signal-Level Approach to Morphing Attacks on Speaker Verification Systems
TD-VIM creates signal-level morphed voice samples that achieve G-MAP attack success rates up to 99.74% against deep-learning and commercial speaker verification systems.
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Audio Spoof Detection with GaborNet
GaborNet replaces sinc functions with Gabor filters in raw-audio neural networks and is tested for audio spoof detection with augmentations in RawNet2 and RawGAT-ST.