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When you speak over a voice assistant, the important change is not just that it stops talking: the system must detect your new turn, stop audio already playing on your device, and keep the conversation history consistent with what you actually heard. OpenAI’s Realtime API documents controls for this behavior, but interruption support alone does not establish that every implementation is full duplex in the strict engineering sense.
Can you interrupt a voice AI while it is talking?
In the OpenAI Realtime API, yes: with voice activity detection (VAD) configured and interrupt_response enabled, a detected start of user speech can cancel an ongoing assistant response in the default conversation. The documented default for this setting is true. Set it to false to let the response continue. See the OpenAI Realtime API reference.
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This is a turn-control behavior, not proof that every part of the audio pipeline stops at the same instant. The server can cancel generation, while the client application controls audio queued or playing locally. The API’s server-events documentation says the client may use a speech-start event to interrupt playback or provide visual feedback: Realtime server events.
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What happens when you speak over the assistant?
- Your audio reaches the active session. The Realtime API supports calls over WebRTC, WebSocket, and SIP. The session’s turn-detection configuration determines how incoming speech is interpreted.
- The detector recognizes speech starting. In server VAD mode, the server emits
input_audio_buffer.speech_started. - The response is cancelled if interruption is enabled. With
interrupt_response: true, the detected speech start cancels an ongoing response in the default conversation. If the setting is false, the response can continue. - Your application stops its playback. The client needs to react to the event to stop locally playing or queued assistant audio. A server cancellation cannot, by itself, retract sound already rendered by a device.
- The client reconciles conversation history. If only part of an assistant audio item played, the client can send
conversation.item.truncatewith the item identifier and playback duration. The server returnsconversation.item.truncatedand removes the transcript associated with unheard audio, so later turns do not treat those words as heard. - The next response waits for the new turn boundary. Depending on configuration, the API can create a response when it determines that you have finished speaking, or the application can trigger one manually.
How does the system know when you have finished speaking?
The main choice is between silence-based detection, an estimate of conversational completion, and application-controlled turns. The OpenAI Realtime API reference documents the following defaults and controls; they are configuration values, not measured guarantees of speed or accuracy.
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| Turn detection | How it decides | Documented controls | Practical trade-off |
|---|---|---|---|
| Server VAD | Uses audio volume to detect speech and silence to detect its end. | The reference gives a default silence_duration_ms of 500 ms, a default threshold of 0.5, and default prefix padding of 300 ms. |
A shorter silence duration can prompt a quicker response, but may mistake a brief pause for the end of your turn. A higher threshold requires louder speech to activate and may help in noisy environments. |
| Semantic VAD | Estimates whether you have finished, using the audio and conversational context. | Eagerness can be low, medium, or high, with maximum timeouts of 8, 4, and 2 seconds respectively; auto is equivalent to medium. |
Less eager behavior allows more time for pauses or trailing-off speech; more eager behavior waits less. The reference notes that semantic detection may have higher latency. |
| Manual turn control | The application, rather than automatic detection, decides when a user turn ends. | Set turn detection to null; the client triggers a response manually. |
Offers application-level control but requires the application to manage turn boundaries and response triggering. |
These settings affect a real trade-off: reacting quickly can mean cutting in during a pause, while waiting longer can make an exchange feel less immediate. The right starting point depends on the acoustic environment and how people speak in the application. The Realtime server events documentation describes the VAD and semantic VAD controls.
Why playback and conversation history must stay in sync
A streaming assistant can generate more speech than has reached the listener. If you interrupt halfway through a sentence, the generated response and the audible response are no longer the same. Keeping the full generated text in conversation history could lead the assistant to rely on words you never heard.
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Truncating the audio item at the client’s playback position addresses that mismatch: the server removes the unheard audio and its transcript from context. In practice, an application needs to track the active assistant item and how much of it played, stop playback on interruption, and report the played duration. The API documents the truncation event and its purpose; it does not prescribe a universal buffering strategy or guarantee a particular audible-stop time.
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For a listener, “full-duplex voice” can describe an interaction in which you can begin speaking before the assistant finishes, and its output may be interrupted so the exchange can continue. The Realtime API documents real-time communication interfaces and interruption controls that support this kind of experience.
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That behavior should not be confused with a technical certification that every implementation uses simultaneous independent send-and-receive audio channels or meets a particular engineering definition of full duplex. The API documentation establishes the controls and interfaces, not that broader claim.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to compare when evaluating an implementation
- Turn boundaries: Does it use silence-based detection, semantic completion estimates, or manual control?
- Responsiveness and unwanted cutoffs: How does it handle short pauses, background noise, and hesitant speech? The documented controls offer ways to tune behavior, but the API reference does not publish comparative accuracy results.
- Playback and state consistency: Does the client stop its output and report how much of an interrupted response actually played?
- Connection interface: The API documents WebRTC, WebSocket, and SIP. The reference does not rank them for latency, reliability, or cost.
The OpenAI Realtime API references describe configuration and intended event behavior, not comparative tests. They do not establish end-to-end interruption latency, interruption accuracy, user preference, or a performance ranking among transports.
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