SpeakerRPL v2 reduces equal error rate from 1.28% to 0.09% (93% relative reduction) on a Vox1-O-like test set by integrating reciprocal points learning with logit normalization, adaptive anchor learning, and model fusion.
3d-speaker: A large-scale multi-device, multi-distance, and multi-dialect corpus for speech representation disentangle- ment
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SpeakerRPL v2: Robust Open-set Speaker Identification through Enhanced Few-shot Foundation Tuning and Model Fusion
SpeakerRPL v2 reduces equal error rate from 1.28% to 0.09% (93% relative reduction) on a Vox1-O-like test set by integrating reciprocal points learning with logit normalization, adaptive anchor learning, and model fusion.