VAD to the Bone: Ultra-Tiny Speech Activity Detection for Edge Deployment
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TL;DR - kiloVAD is a 2.1K-parameter, CNN-only voice activity detector designed for causal embedded inference. It achieves 0.850 AUC on AVA-Speech while using standard, deployment-friendly components.
- Uses standard Mel features and avoids recurrent layers, learnable filterbanks, and non-causal post-processing.
- Combines per-layer structured pruning with self-distillation.
- Angle-based quantization-aware training improves results by 1–4% over standard QAT.
- Operates with 200 ms of context under causal, per-frame evaluation.
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VAD to the Bone: Ultra-Tiny Speech Activity Detection for Edge Deployment
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TL;DR - kiloVAD is a 2.1K-parameter, CNN-only voice activity detector designed for causal embedded inference. It achieves 0.850 AUC on AVA-Speech while using standard, deployment-friendly components.
- Uses standard Mel features and avoids recurrent layers, learnable filterbanks, and non-causal post-processing.
- Combines per-layer structured pruning with self-distillation.
- Angle-based quantization-aware training improves results by 1–4% over standard QAT.
- Operates with 200 ms of context under causal, per-frame evaluation.