Glossary
End-pointing is the real-time detection of when a speaker has finished an utterance. It's the trigger that tells a voice assistant "OK, the user is done, send the request" or a live caption system "finalize this caption now." Sounds simple — turns out it's hard. Humans pause mid-sentence to think, breathe, search for words. Cutting them off feels rude; waiting too long feels sluggish. Good end-pointing is the difference between Siri feeling responsive and Siri feeling broken.
Naive vs smart end-pointing
Naive: trigger after N milliseconds of silence (typical: 500-800 ms). Cuts off slow speakers and people pausing to think. Smart: combine silence detection with a neural model that predicts "is this utterance complete?" based on syntax, pitch contour (falling pitch = end of statement, rising = question), and lexical signals ("...the answer is" = expecting more).
End-pointing vs VAD
VAD answers "is this frame speech or silence?" — frame-level binary classification. End-pointing answers "has the user finished their utterance?" — semantic, context-dependent. They're related but separate. VAD is a building block of end-pointing; you need both for production live ASR.
Why batch transcription doesn't need it
Batch (file-upload) transcription has the entire audio at once — no need to predict when speech is done. End-pointing is a real-time-streaming-only concern. We're a batch service, so end-pointing isn't part of our pipeline. If you're integrating live captions or building a voice assistant, you'll need a streaming ASR system with end-pointing built in.
In practice
You ask Siri "What's the weather—" then pause to phrase the rest. Naive end-pointing fires after 500 ms and Siri responds with current weather (wrong location). Smart end-pointing notices the falling pitch wasn't sentence-final and the word "weather" usually takes a modifier — waits another second and gets your full "What's the weather in Tokyo tomorrow?"
Related terms
See batch transcription
Get started — freeLast updated: September 20, 2026
Frequently Asked Questions
What does End-pointing mean in transcription?
Real-time detection of when a user has stopped speaking — the trigger for finalizing live captions or sending dictation results to downstream systems.
Why does End-pointing matter when choosing a transcription workflow?
End-pointing affects how you understand transcript quality, timing, compatibility, or the technology behind speech-to-text results.
Where can I apply End-pointing on Transcript.you?
Use the related workflow at /ai-transcription when you want to see how this glossary concept connects to an actual transcription task.
Browse the full transcription glossary or read the complete guide to AI transcription.