Does per-frame early exit pay? A compute-matched study of dynamic depth for on-device speech enhancement
Deep learning-based speech enhancement is increasingly deployed on-device in hearing aids, headsets, and earbuds. Most of these devices, however, can only accelerate static int8 graphs, so a depth-varying network must be implemented as several graphs, orchestrated by a policy. In this paper, we supervise every intermediate depth of one causal model, then we fine-tune its output heads to guarantee that deeper outputs are never worse than shallower ones. Using this training protocol, we can derive a family of static models that are more Pareto-efficient than their equivalently-sized counterparts trained from scratch on the same budget. Specifically, we achieve up to 0.11 higher PESQ for equ...
| Authors | Clément Laroche, Riccardo Miccini |
|---|---|
| Primary category | eess.AS |
| Published | 24 September 2026 |
| Last updated at source | 24 September 2026 |
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