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Curriculum Learning with Contrastive Loss for Lightweight Speaker Verification
Jin Li (Hong Kong Polytechnic University), Man‐Wai Mak (Hong Kong Polytechnic University), Johan Rohdin (Brno University of Technology), Oldřich Plchot (Brno University of Technology)
Abstract: We propose a novel training framework for lightweight speaker verification (SV) systems, which are often deployed on mobile devices with constrained storage resources. Our method leverages supervised contrastive loss to learn speaker-discriminative embeddings. To enhance negative sample selection, crucial in contrastive learning, we introduce CurriNeg, a curriculum learning strategy that selects negative pairs based on their difficulty. A teacher network assigns difficulty scores to negative pairs, guiding a student network to learn progressively from easy to hard negative pairs. Additionally, we propose to leverage AM-Softmax’s capability to form multi-class boundaries to compensate for the absence of multi-class optimization in the contrastive loss function, which results in the CurriNeg-AMS loss. Experiments on VoxCeleb1 show that our method reduces relative equal error rate by 8.6% and 20.2% over the X-vector and Fast ResNet34 baselines, respectively. Code is available at: GitHub.1