A gated mixture of per-layer adapters and prompts outperforms full fine-tuning on three speaker verification benchmarks while updating only 5.4% of the model.
Deep speaker embedding extraction with channel-wise feature responses and additive supervision softmax loss function
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UniPET-SPK: A Unified Framework for Parameter-Efficient Tuning of Pre-trained Speech Models for Robust Speaker Verification
A gated mixture of per-layer adapters and prompts outperforms full fine-tuning on three speaker verification benchmarks while updating only 5.4% of the model.