An input-conditioned layer selector, trained with top-k gating, lets speech foundation models drop encoder layers per sample while outperforming random dropping and matching early exit on four audio benchmarks.
We utilized well- known transformer-based foundation models: (i) WavLM
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.SD 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
Input Conditioned Layer Dropping in Speech Foundation Models
An input-conditioned layer selector, trained with top-k gating, lets speech foundation models drop encoder layers per sample while outperforming random dropping and matching early exit on four audio benchmarks.