MKD-FSV: Multi-Layer Knowledge Distillation for Far-Field Speaker Verification

Existing Automatic Speaker Verification (ASV) systems show inferior performance in dealing with the cross-domain challenges posed by far-field utterances. To mitigate domain mismatch issues in far-field scenarios, this paper proposes a novel but promising Multi-layer Knowledge Distillation-based Far-field Speaker Verification (MKD-FSV) system. MKD-FSV employs a teacher-student framework with a well-thought-out Multi-layer Knowledge Distillation Strategy (MKDS) to transfer domain-invariant knowledge from close-talking utterances, guiding the student model to effectively learn far-field data. MKDS elaborately incorporates Feature Knowledge Distillation (FKD) with Decoupled Knowledge Distillation (DKD) in a complementary manner to ensure a more comprehensive knowledge transfer and significantly enhance the system’s performance. Specifically, FKD captures domain-invariant speaker characteristics more effectively, transferring crucial features from the teacher to the student model to develop a more robust, generalized speaker embedding space, while DKD enhances speaker feature discriminability by balancing information between target and non-target classes, reducing interference, and improving accuracy in cross-domain mismatch scenarios. This dual approach ensures that the student model inherits robust speaker embeddings while refining its decision-making, leading to improved verification performance. Moreover, a reparameterization technique is utilized to reduce model complexity and enhance inference efficiency. Extensive experimental results demonstrate that MKD-FSV outperforms existing methods in far-field ASV tasks, achieving both higher verification accuracy and significantly improved inference efficiency through the reparameterization technique, making it highly applicable in complex real-world scenarios.