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Build these as three separate controls: turn detection decides when an answer is finished, barge-in lets the interviewee stop the bot while it is speaking, and application logic enforces the interview’s overall deadline. OpenAI’s Realtime API documents mechanisms for each, but its session-duration cap is not a substitute for an interview timer, and the documented example settings are not proven interview recommendations.
Separate answer turns, interruptions, and the interview clock
Voice activity detection (VAD) detects when a person starts or stops speaking; it does not determine how long the entire interview should last. Treat the controls as distinct:
- Turn detection: decides when an individual answer has ended and the bot may respond.
- Interruption handling: stops the bot’s active response and queued audio when the interviewee begins speaking.
- Application deadline: determines when the interview must end, regardless of turn detection.
OpenAI describes VAD as a way for the Realtime API to detect when a user has started or stopped speaking in its Voice activity detection (VAD) guide. The API separately documents a maximum Realtime session duration of 60 minutes in its Realtime conversations guide. That is a platform limit, not a recommended interview length.
Choose how the bot decides an answer is finished
The Realtime API offers server VAD and semantic VAD for automatic turn detection. If your workflow requires an application check before the bot responds, you can keep VAD enabled while disabling automatic response creation, then trigger the response after your application approves the input. You can also disable VAD for fully manual audio turn control. The available modes and settings are described in the VAD documentation.
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Server VAD: end a turn after silence
Server VAD uses an audio threshold to detect speech and a silence duration to decide when speaking has stopped. Its controls include an activation threshold, prefix padding, and silence duration. The documentation’s example uses a threshold of 0.5, prefix padding of 300 ms, and silence duration of 500 ms. These are example or default values, not settings validated as optimal for interviews.
A shorter silence duration can reduce the wait before a reply, but it can also make the bot mistake a thinking pause for the end of an answer. If interviewees are likely to pause mid-thought, test longer waits with representative audio rather than assuming that the quickest response is best.
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Semantic VAD: estimate whether the thought is complete
Semantic VAD uses an estimate of whether the person has completed an utterance rather than relying only on silence. Its eagerness setting controls how readily it concludes that a turn is over: low eagerness gives the speaker more time, while high eagerness tends to divide speech into turns sooner. The auto setting is equivalent to medium, according to the VAD guide.
Consider lower eagerness when an interview calls for reflective answers and people may pause to think; consider higher eagerness only when shorter chunks fit the interaction. Neither choice is universally right: tune it against the answer style you expect and check for both premature cutoffs and excessive dead air.
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Let interviewees interrupt bot speech cleanly
For barge-in, configure the interaction so detected user speech can cancel an active model response. OpenAI’s interruption guidance describes VAD detecting the new speech, cancelling the ongoing response, and starting a new one. Cancelling generation alone is not enough: the audio already queued for playback also has to stop, and the conversation history must reflect what the person actually heard.
| Transport | Who handles output audio | What interruption handling needs |
|---|---|---|
| WebRTC or SIP | The server manages the output audio buffer. | The server truncates audio that had not played when the interruption occurred. |
| WebSocket | The client owns playback. | The client stops playback and manages truncation of the unplayed response. Track how much audio actually reached playback so unplayed audio is not left in conversation history as though it were heard. |
Exercise interruptions at the beginning, middle, and end of a spoken question. Also test background speech that should not trigger barge-in, and a new answer arriving while old audio is still queued. The documentation explains the mechanisms but does not establish interview-specific performance across noise conditions or populations.
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Use an idle timeout for pauses, not for the interview deadline
The server-VAD idle_timeout_ms setting addresses an unexpected pause after the assistant’s last response audio has finished playing. When the timeout expires, the service emits a timeout event and triggers a response, as described in the idle-timeout guide. It is separate from the duration limit for the interview.
Choose the pause behavior to fit the interview. For example, the bot can offer a brief, neutral prompt to continue or end the session. Do not treat silence as consent to advance to another question.
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Enforce total interview duration in application logic
Track elapsed interview time with a monotonic clock in your application. When the configured deadline arrives, stop queuing new questions, decide whether to let an answer in progress reach a safe boundary, deliver a concise closing, and finalize the session. Define what happens if the deadline arrives during bot speech as well as during an interviewee’s answer.
The Realtime API’s documented maximum session duration is 60 minutes; the session documentation presents it as a service limit. Choose the interview’s own duration according to your workflow and enforce it independently.
Keep the bot quiet when audio is not an answer
Silence, background noise, hold music, or side conversations can otherwise prompt an unnecessary response. OpenAI’s voice prompting guide recommends giving the model a no-op wait tool so it can remain silent and keep listening. For an interview, also instruct the bot not to fill thinking pauses with reassurance or move to the next question just because it hears non-directed speech.
Evaluate the controls with representative audio
The API documentation describes configurable mechanisms, not a tested interview configuration or benchmark for an optimal silence duration. Before deployment, evaluate the interaction with answer styles and acoustic conditions resembling your use case. Track:
- Premature turn endings and excessive dead air.
- Time from the end of an answer to the bot’s first reply.
- False barge-ins caused by background or overlapping speech.
- Whether interruption stops playback and the conversation recovers coherently.
- Whether the interview closes at its configured deadline, including when a turn is in progress.
Use those results to adjust the detector, response policy, and playback handling; do not describe the documented example values as empirically optimal without evaluation of your own.
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