EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting

Oguzhan Buyuksolak, Alican Gok, Osman Erman Okman
1/22/2026
eess.AScs.CLcs.SD

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Abstract

We introduce an efficient few-shot keyword spotting model for edge devices, EdgeSpot, that pairs an optimized version of a BC-ResNet-based acoustic backbone with a trainable Per-Channel Energy Normalization frontend and lightweight temporal self-attention. Knowledge distillation is utilized during training by employing a self-supervised teacher model, optimized with Sub-center ArcFace loss. This study demonstrates that the EdgeSpot model consistently provides better accuracy at a fixed false-alarm rate (FAR) than strong BC-ResNet baselines. The largest variant, EdgeSpot-4, improves the 10-shot accuracy at 1% FAR from 73.7% to 82.0%, which requires only 29.4M MACs with 128k parameters.

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Cite this paper

@article{buyuksolak2026edgespot,
  title  = {EdgeSpot: Efficient and High-Performance Few-Shot Model for Keyword Spotting},
  author = {Oguzhan Buyuksolak and Alican Gok and Osman Erman Okman},
  year   = {2026},
  eprint = {2601.16316},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.16316v1}
}

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