Before an assistant can understand you, it has to answer a simpler question: are you even speaking right now? It listens to the loudness and decides "talking" or "silence" — and figures out when your sentence ends so it can reply. Let's build that detector.
The green line is how loud it is right now. The yellow line is your threshold: louder than it = "talking", quieter = "silence". After enough silence, the VAD decides your sentence ended. Tune the two sliders!
threshold — too low → triggers on noise; too high → misses quiet talking
silence timer (the "hangover") — how long of quiet = end of sentence
It thinks the fan or your breathing is "talking" — false triggers. Wastes battery, records noise.
It misses your quiet words — clipping the start of your sentence.
Too short → it cuts you off during a pause. Too long → it feels slow to reply. This is endpointing.
The loudness threshold you built is the idea — simple enough to see how VAD works. But it's easily fooled by loud rooms and music. Real products use smarter methods:
✅ Tiny, instant, runs anywhere. ⚠️ Picks a fixed loudness line — noise, music, or a fan can fool it.
Checks the frequencies of speech, not just loudness (e.g. WebRTC VAD in browsers/calls). ✅ Still light & fast. ⚠️ Struggles in very noisy places.
A small deep-learning model that spots speech even inside loud noise or music. ✅ Very accurate. ⚠️ Needs a model (a bit heavier).
You built a Voice Activity Detector — the first thing every voice assistant runs: knowing when you're talking, and when your sentence has ended (endpointing).