VAD to the Bone: Ultra-Tiny Speech Activity Detection for Edge Deployment

Abstract:Voice activity detection (VAD) triggers downstream speech processing in always-on systems under strict memory, latency, and compute constraints. Recent compact models report strong accuracy but rely on components that are not widely supported: learnable filterbanks, recurrent layers, or non-causal post-processing. We propose kiloVAD, designed for embedded inference using standard Mel features, CNN-only layers, and tunable context/spectral parameters. We introduce per-layer structured pruning with self-distillation and angle-based quantization-aware training (QAT) that outperforms standard QAT by 1-4%. Evaluated per-frame under causal conditions, kiloVAD achieves 0.850 AUC on AVA-Speech with 2.1 k parameters and 200 ms context, establishing a new state of the art for causal, deployment-ready VAD.

Audio and Speech Processing (eess.AS); Machine Learning (cs.LG)

arXiv:2607.25870 [eess.AS]

(or arXiv:2607.25870v1 [eess.AS] for this version)

https://doi.org/10.48550/arXiv.2607.25870 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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