Author

Clément Laroche

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#machine learning Preprint Sep 2026

Does per-frame early exit pay? A compute-matched study of dynamic depth for on-device speech enhancement

This paper supervise every intermediate depth of one causal model, then fine-tune its output heads to guarantee that deeper outputs are never worse than shallower ones, and can derive a family of static models that are more Pareto-efficient than their equivalently-sized counterparts trained from scratch on the same bud...

Clément Laroche, Riccardo Miccini · 0 citations

#machine learning Preprint Sep 2026

Beyond Model Size: Redesigning LiSenNet for embedded speech enhancement

Deploying real-time speech enhancement on resource-constrained devices requires meeting strict latency, memory, and energy constraints. Microcontroller NPUs can accelerate neural inference under these constraints, but only through a restricted set of operators in static, integer-quantized graphs. Recent speech-enhancem...

Clément Laroche, R. Olsson · 0 citations