Learning from imperfect teachers for low-resource acoustic generalization
Knowledge distillation (KD) improves low-resource acoustic learning by enriching one-hot supervision with the softened predictive distribution of a fixed teacher network. However, a teacher trained with limited or imbalanced annotations may produce a biased distribution whose components are not uniformly reliable. Although this distribution can still encode useful knowledge, direct full-distribution matching may also transfer teacher-induced biases, thereby distorting the student's decision boundary and degrading its generalization performance. To address this limitation, we propose Boundary-Anchored Mass-Partitioned Distillation (BA-MPD), a logit-based distillation objective composed of ...
| Authors | Shuanglin Li, Ruxiao Qian, Jian Liu et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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