Why it matters

Good VAD makes voice apps feel responsive. Bad VAD wastes compute or feels laggy.

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The architecture

Signal analysis: energy, spectral features distinguish speech from noise.

Neural VAD: modern ML models robust to noise.

VAD approachesSignal featuresenergy, spectralNeural VADSilero, WebRTC VADSpeech / silence outputwindows or framesWebRTC VAD lightweight for browsers; Silero more accurate but heavier
VAD methods.
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How it works end to end

WebRTC VAD: lightweight built into WebRTC. Works well for clear voice; less good in noise.

Silero VAD: PyTorch model. Excellent noise robustness. Popular open choice.

Sensitivity tuning: threshold between speech and non-speech. Too sensitive: false triggers. Too strict: cut speech starts.

Endpoint detection: know when speaker is done, for turn-taking.