Definition
What is end-of-turn detection?
By Lewis CrookPublished
End-of-turn detection is the mechanism by which a voice AI decides the caller has finished speaking and it should begin to respond. It combines voice activity detection, semantic completion signals, and timing heuristics. It is usually the largest single contributor to turn-taking latency.
End-of-turn detection is the bot deciding it is its turn to speak.
Why it matters for enterprise CX leaders
- Aggressive end-of-turn detection interrupts callers mid-sentence; conservative settings produce awkward pauses.
- Modern semantic end-of-turn models outperform pure silence detection on most accents and intent types.
- Tuning end-of-turn behaviour is the single highest-leverage UX intervention in voice AI deployments.
Frequently asked questions
- Why is end-of-turn detection hard?
- Because callers pause mid-sentence to think, and silence does not reliably signal a finished turn. Pure timeout-based detection trades off interruption against responsiveness.
- Can end-of-turn detection be tuned per intent?
- Yes, and it should be. Disclosure scripts tolerate longer pauses; transactional intents benefit from faster turn-taking.
- How much latency does end-of-turn detection add?
- Typically 200–800 ms depending on configuration. It is usually the largest single component of turn-taking latency.
Related terms
- Turn-taking latency— Turn-taking latency is the awkward pause before the bot starts talking back.
- Voice AI latency— Voice AI latency is the gap before the system starts talking back.
- Barge-in— Barge-in lets the caller interrupt the bot without breaking the conversation.
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