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arXiv · 2607.25289

AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition

Yuqi Li·Yi-Cheng Lin·Xianglong Wang·Kuo Yang·Xiaoqin Feng·Yixuan Wang·Huiran Duan·Yingli Tian
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Abstract

On-device speech emotion recognition (SER) is critical for real-time applications, yet large self-supervised models that excel at SER are too costly for edge devices. Multi-teacher knowledge distillation can compress them into a lightweight student, but two challenges remain: teacher reliability varies across batches, and logit-level distillation ignores inter-sample relational structure. We propose Adaptive Multi-teacher Relational Distillation (AMRD) to address both. A one-class SVM on each teacher's logit similarity matrix assigns per-batch weights favoring more coherent teachers. A relational distillation loss aligns teacher and student similarity matrices, capturing structure that logit matching misses. On IEMOCAP and CREMA-D datasets across four student architectures, AMRD outperforms single-teacher distillation baselines in most settings, and ablations confirm both components yield complementary gains.

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Yuqi Li, Yi-Cheng Lin, Xianglong Wang, Kuo Yang, Xiaoqin Feng, Yixuan Wang, Huiran Duan, Yingli Tian. 2026-07-28. AMRD: Adaptive Multi-Teacher Relational Distillation for Lightweight Speech Emotion Recognition. https://arxiv.org/abs/2607.25289

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