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Build turn-taking, interruption handling, and interview length as three separate controls. A voice activity detector can decide when an answer seems finished; barge-in handling can stop the bot when someone starts speaking; and an application timer can end the interview at a chosen deadline. The OpenAI Realtime API illustrates these patterns, but other voice platforms may use different settings and playback behavior.
1. Decide how the bot detects the end of an answer
Voice activity detection (VAD) detects when speech starts and stops; it is not a timer for the whole interview. In an automatic conversation, leave VAD enabled and choose whether turn endings should depend on silence or on an estimate that the person has completed an utterance. OpenAI documents both approaches in its Realtime API voice activity detection guide.
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Server VAD: end a turn after silence
Server VAD treats a sufficiently long quiet interval as the end of a turn. Its controls include an activation threshold, prefix padding, and silence duration. The official example uses a 0.5 threshold, 300 ms of prefix padding, and a 500 ms silence duration; these are example or default API values, not validated settings for interviews.
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A shorter silence duration can make the bot respond sooner, but it can also mistake a thinking pause for the end of an answer. Tune it against the kinds of answers you expect, including pauses and longer responses, rather than assuming one value works for every interview.
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Semantic VAD: estimate whether the speaker is finished
Semantic VAD uses a model’s estimate of whether the person has completed an utterance and offers an eagerness setting. Lower eagerness gives the speaker more time; higher eagerness tends to split turns sooner. The API’s auto setting is equivalent to medium. If interviewees may pause to think, allow more time and evaluate the result with representative answers.
Choose who is allowed to trigger a response
Automatic response creation is convenient when the bot can reply as soon as a turn is detected. If your workflow needs application validation or moderation first, keep VAD on but disable automatic response creation; have your application trigger a response only after it approves the input. The API also supports turning VAD off for fully manual audio-turn control.
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2. Let interviewees interrupt bot speech
For natural turn-taking, the bot must be able to stop speaking when the interviewee begins. In the Realtime API, VAD can detect incoming speech during an active response, cancel that response, and start a new one. Stopping the model response is only part of the job: audio already queued for playback must also be handled correctly. See OpenAI’s Realtime conversation interruption guidance.
WebRTC or SIP: output buffering is server-managed
With WebRTC or SIP, the server manages the output audio buffer and truncates audio that was not played when an interruption occurs.
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With WebSocket playback, the client must stop playback and manage truncation of the unplayed output. Track how much audio actually reached playback; otherwise, conversation history can imply that the interviewee heard words that were never played. Removing that unplayed tail keeps the recorded conversation aligned with what happened.
Test interruptions under realistic conditions
Check barge-in behavior when someone interrupts near the beginning, middle, and end of a question. Also test background speech that should not interrupt, and a new answer arriving while audio from the bot is still queued. OpenAI documents the mechanisms, but the cited documentation does not establish interview-specific accuracy across noise conditions or populations.
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3. Separate silence recovery from the interview deadline
An idle timeout handles an unexpected pause. A total-duration deadline enforces the interview’s end time. They solve different problems and should be implemented separately.
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The Realtime API’s server-VAD idle_timeout_ms setting is measured after the last assistant response audio has finished playing. When it expires, the service emits a timeout event and triggers a response. For an interview, make that response a brief, non-coercive offer to continue or end; silence should not be treated as consent to advance automatically. See the OpenAI VAD documentation on idle timeouts.
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Enforce the total duration in application logic
Track elapsed interview time in your application with a monotonic clock, which measures elapsed time rather than relying on a wall-clock value that can change. At the configured deadline, stop queuing new questions, decide whether to let an answer already in progress reach a safe boundary, then deliver a concise closing and finalize the session. Define what happens if the deadline arrives while the bot is speaking or the interviewee is answering.
OpenAI states that a Realtime session can last at most 60 minutes in its Realtime conversations documentation. That is a platform limit, not a recommended interview length or a replacement for your own deadline.
4. Keep silence and background audio from advancing the interview
Silence, hold music, room noise, or a side conversation can lead a voice model to respond when it should keep listening. OpenAI’s voice prompting guide recommends giving the model a no-op wait tool so it can remain silent. For an interview, also instruct it not to fill pauses with reassurance or move to the next question merely because audio is present; it should respond to the interviewee’s directed speech.
5. Evaluate the interaction before deployment
The API settings provide mechanisms, not a proven interview configuration. Test with representative audio and track:
- Answers cut off before the speaker is finished.
- Excessive dead air after an answer.
- False interruptions caused by background speech or noise.
- Time from the end of an answer to the bot’s first reply.
- Whether interruption stops playback cleanly and the conversation recovers.
- Whether the application ends the interview at its configured deadline.
Include short and long answers, thinking pauses, room noise, and overlapping speech. Treat documented sample values as starting points for evaluation, not evidence that a configuration is optimal.
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