Fast Speech Foundation Model Distillation Using Interleaved Stacking
Distilling a large speech foundation model (SFM) into an efficient student model has been successfully applied to low-resource environments. Although distillation reduces inference latency, it requires an additional student model training. However, the training efficiency of SFM distillation remains underexplored. In this work, we explore training acceleration of SFM distillation to speed up model deployment. We examine the potential of stacking, in which the model depth is progressively increased through training until the target model depth is reached. While existing stacking methods improve training speed, they suffer from performance degradation. To handle this limitation, we propose ...
| Authors | Eungbeom Kim, Kyogu Lee |
|---|---|
| Primary category | eess.AS |
| Published | 10 June 2026 |
| Last updated at source | 16 June 2026 |
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