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Data-Efficient Semi-Supervised Few-Shot Speaker Verification via Prototype Space Optimization
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DOI：10.1109/LSP.2025.3648641.png)
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Abstract
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Speaker verification technology has widespread applications across many domains, benefiting from deep learning advancements. However, due to the high cost of acquiring labeled data, semi-supervised learning has emerged as a prominent research focus. Current semi-supervised learning frameworks commonly suffer from two limitations: 1) the labeled data distribution is often restricted, and 2) they still rely on a considerable amount of labeled data. To address these issues, we propose three different distribution scenarios of labeled data and construct a general semi-supervised framework. Furthermore, to enhance the guidance efficacy of limited labeled data, we innovatively employ prototype space optimization to strengthen the model’s discriminative capability under low-resource scenarios. Experimental results demonstrate that on the Vox1-o test set, our approach achieves a 41.7% relative reduction in equal error rate compared to self-supervised baselines, and a 28.9% improvement over conventional semi-supervised framework baselines.
Keywords:
Speaker verification
semi-supervised learning
DINO
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3.9
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808
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