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A reliable voice assistant must treat an interruption as two events at once: an audio-playback event and a conversation-state event. When the user starts speaking, stop or cancel the assistant’s response promptly, remove any unplayed speech from the conversation history, and preserve the application task state needed to answer the new request. A stateful speech-to-speech Realtime session is the most direct architecture for browser assistants that need barge-in, natural turn taking, session context, and tool use. A chained speech-to-text, text-agent, and text-to-speech design can be a better fit when you already operate a text agent or need tighter control over each stage.
This guide presents an implementation model for browser and application developers, including transport choices, turn detection, interruption recovery, follow-up continuity, prompting, security, and a practical test plan.
What interruption handling actually requires
“Stop talking when I begin speaking” is only the visible part of barge-in. A correct implementation must also reconcile what the assistant said with what the user could hear. If generated audio remains in the model’s history after playback was stopped, the model may assume the user heard information that never reached them. The next answer can then refer to an unheard sentence, produce an apparently contradictory reply, or skip an explanation the user still needs.
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Model the interaction as two related state machines:
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- Media state: capturing microphone audio, generating output, buffering audio, playing audio, stopping playback, and detecting a new speech segment.
- Conversation and task state: the turns the user and assistant actually completed, active tool calls, confirmed values, pending actions, and durable application data.
Barge-in transitions both machines. The media state stops or clears output. The conversation state cancels the active response and truncates assistant output to the portion that was actually played. The application task state is usually retained, because the user may be correcting or continuing the same task.
Choose an architecture before writing the client
Stateful speech-to-speech Realtime session
A Realtime session keeps an ongoing audio conversation with session state, tool use, and turn detection in one interaction model. OpenAI’s “Getting started with the Realtime API” describes this path as a starting point for voice agents that need barge-in, low first-audio latency, natural turn taking, and realtime tool use. The browser connects to a session created by your server, rather than exposing a long-lived API credential in page code.
This approach minimizes the number of handoffs between speech recognition, reasoning, and speech synthesis. It is a strong default when the primary product experience is spoken conversation and the assistant must react while the user is still engaged in a live session.
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A chained design records or streams speech to a speech-to-text component, sends text to an existing agent, and passes the response to text-to-speech. It can fit an application that already has a mature text agent, retrieval layer, moderation pipeline, or provider-specific speech components. Each stage is independently observable and replaceable, but your application must coordinate turn boundaries, cancellation, playback queues, and state across those stages.
Do not choose by assuming one architecture is universally faster or better. Decide based on whether you need a native speech-to-speech session, how much control you need over recognition and synthesis, which components you already operate, and where you want to own conversation persistence and interruption logic. OpenAI’s “Audio and voice” documentation describes these as alternative workflow patterns rather than a single mandatory design.
Decision matrix
| Question | Stateful speech-to-speech | Chained pipeline |
|---|---|---|
| Primary use | Live spoken conversation with integrated session behavior | Reuse of an existing text agent and separate audio services |
| Turn detection | Session or SDK VAD, or application-managed boundaries | Your speech-to-text and orchestration layer must coordinate boundaries |
| Barge-in ownership | Transport and session can cancel and reconcile a response | Your client or media service must stop synthesis and update history |
| State model | Live conversation context plus your durable task state | Explicit state transfer between recognition, agent, and synthesis stages |
| Best fit | Browser or application designed around continuous voice | Existing text-first systems or teams needing stage-by-stage control |
Browser prerequisites and secure session setup
The documented WebRTC browser flow requires a server runtime for the session-creation example, a project API key kept on a trusted server, microphone permission, and a secure origin: HTTPS in production or localhost during development. A built-in microphone is sufficient for prototyping; a dedicated USB microphone is optional equipment, not a software requirement.
- Create the session on your server. Your backend authenticates with the provider and requests an ephemeral client credential for the browser session. Never place the project API key in JavaScript shipped to users.
- Request microphone access. The page must call the browser media-permission flow and handle denial, missing devices, and a device that becomes unavailable.
- Establish WebRTC. Negotiate the peer connection, attach the microphone track, and use the negotiated media path for assistant audio.
- Open the session event channel. Use the data channel for session events, user-interface status, tool events, and application commands.
- Track lifecycle errors. Surface permission failures, expired ephemeral credentials, failed negotiation, disconnected peers, and a session that reaches its limit rather than leaving the user staring at a silent interface.
The WebRTC guide recommends WebRTC for browser or mobile client connections because it provides more consistent performance than using WebSockets for the media path. The recommendation does not remove the need to design reconnection and state recovery.
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Assign transport responsibilities explicitly
WebRTC and SIP
With WebRTC and SIP, the server manages the output audio buffer and automatic truncation behavior described in the Realtime conversations documentation. Your client still needs to update its interface, detect local device failures, and keep application task state, but it does not need to implement the same low-level playback accounting required by a raw WebSocket audio client.
WebSocket
WebSocket gives the application more direct control, but that control becomes implementation work. The client captures audio, sends it, receives generated audio, queues and plays that audio, stops playback when the user barges in, and reconciles the conversation with the amount of audio that was actually heard. The Agents SDK documentation describes an interruption event and the local playback and history responsibilities associated with this route.
Write down ownership before coding. For every operation, answer:
- Who detects the start of user speech?
- Who cancels the active response?
- Who empties queued audio?
- Who measures the played portion of the assistant output?
- Who records the truncation in conversation history?
- Who retains the application task and tool state?
Ambiguous ownership causes the common failure in which the speaker hears silence but the model continues believing its response was delivered.
Design turn detection deliberately
Semantic VAD
Semantic voice activity detection aims to infer whether the speaker has finished a thought, not merely whether the microphone signal became quiet. It can wait longer when a person sounds unfinished, which may produce more natural turn boundaries for conversational speech. It still needs evaluation with your users, microphones, languages, and noise conditions.
Server VAD
Server VAD uses configurable signal thresholds and timing controls. The Agents SDK guide identifies settings such as threshold, prefix padding, silence duration, and idle timeout. Prefix padding preserves a short amount of audio before detected speech; silence duration controls how long the system waits before deciding a turn ended; idle timeout can determine what happens when a session receives no useful speech.
These controls trade responsiveness against premature turn endings. A short silence window can interrupt a speaker who pauses to think. A long window can make the assistant feel slow and can delay a follow-up. There is no universally correct value in the documented material, so tune with representative speakers, pauses, accents, microphone distances, background sound, and people who frequently interrupt.
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Application-managed boundaries
Manual boundaries give your interface explicit control. A push-to-talk button, a visible “send” action, or a domain-specific end-of-turn gesture can be appropriate when accidental activation is costly. Manual control is less natural for an always-listening assistant and does not eliminate the need to stop playback when speech arrives.
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A practical tuning procedure
- Collect recordings or live sessions representing normal speech, short pauses, unfinished phrases, rapid corrections, background media, and side conversations.
- Start with the SDK’s documented defaults for the selected VAD mode rather than inventing values from a benchmark that does not match your environment.
- Measure product outcomes: premature turn splits, delayed responses, missed interruptions, false activations, and clarification requests.
- Adjust one timing or threshold control at a time, then repeat the same scenarios.
- Test on the actual browsers, headsets, speakers, and network conditions your product supports.
Implement barge-in as an ordered state transition
The safest interruption handler is explicit and idempotent. A second speech event, a duplicate network message, or a late audio packet should not restore audio that the user already interrupted.
- Detect speech. Use the selected VAD or an explicit application signal. Mark the active response as interrupting immediately so the UI and playback loop stop accepting new audio for that response.
- Cancel generation. Ask the session or orchestration layer to cancel the active assistant response. In a chained pipeline, cancel text-to-speech and any still-running generation task.
- Stop and clear playback. Stop the audio element or worklet and discard queued chunks that have not started. Do not merely lower the volume; silent queued audio can resume later and speak out of context.
- Calculate what was heard. For a client-managed buffer, maintain a played-byte, sample, or time position tied to the assistant output item. Treat only the played segment as delivered.
- Truncate history. Record the assistant output as truncated at the played position. With WebRTC or SIP, use the server-managed behavior; with WebSocket, perform the client-side reconciliation required by the session protocol.
- Accept the new user turn. Continue capturing the incoming speech and let the session create the next response after the new turn boundary.
- Resume from valid state. Keep confirmed task values and completed tool results, but do not treat interrupted, unheard assistant instructions as user knowledge.
A conceptual client controller can look like this:
onSpeechStart():
response.status = "interrupting"
cancelActiveResponse()
playback.stop()
playedPosition = playback.position()
clearQueuedAudio()
truncateAssistantHistory(response.id, playedPosition)
response.status = "awaiting_user_turn"
onUserTurnComplete(transcript):
conversation.appendUserTurn(transcript)
response.status = "generating"
requestNextResponse()The names above are illustrative application functions, not provider event names. Use the current session protocol and SDK events for the concrete calls.
Keep conversation context separate from task state
Follow-up questions depend on two kinds of continuity. The live conversation context lets the assistant resolve references such as “what was that last thing?” or “change it to Friday.” Application task state lets the product continue work even when a session is interrupted, reconnected, or resumed later.
Conversation context
Keep the turns, tool results, and relevant instructions needed for immediate references. When output is interrupted, the context must represent only the assistant audio that was played. If the assistant began listing three options but the user interrupted after the first, the model should not assume the other two were heard.
Durable task state
Store confirmed facts and workflow progress outside the transient audio stream: selected account, destination, date, draft content, authorization status, completed tool calls, and pending confirmation. The live-session guidance recommends preserving both conversation context and application task state so a user can continue without repeating themselves.
Use a structured task object rather than asking the model to reconstruct everything from prose. For example:
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Mixed voice and typed input
Typing is useful when a user needs to provide an exact identifier, spell a name, paste a code, or clarify a noisy utterance. Send typed text or structured context into the same live interaction, label it as user-provided input, and update task state through the same validation rules used for speech. Do not create a second disconnected conversation that loses the reference established by voice.
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An assistant should not speak in response to every sound. Background conversation, hold music, media playback, silence, and speech not directed at the assistant can use a no-op wait behavior. This reduces false activations and prevents the assistant from narrating ambient audio.
Unintelligible direct speech is different. If the system has evidence that a person addressed the assistant but cannot reliably understand the words, ask for clarification. Keep the prompt short and specific: ask the user to repeat the requested detail, offer a typed-input option when precision matters, and avoid pretending that a low-confidence transcript is correct.
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Correction of the interrupted answer
If the user says “No, I meant next Tuesday” while the assistant is speaking, cancel the response, preserve the previously confirmed task fields, replace only the conflicting field after validation, and generate a response that acknowledges the corrected value. Do not repeat an interrupted explanation unless the user asks for it.
Reference to unheard content
If the user asks “what was the second option?” immediately after interrupting before the second option played, the assistant should say it had not finished presenting the options and then provide the missing information. This behavior follows from truncating history to played audio; it should not claim the user heard text that remained buffered.
New question during an active task
Maintain the task object while answering a side question. Mark the original action as paused, answer using the relevant conversation context, and return to the pending task only when the user signals that they are ready. For high-impact actions, require an explicit confirmation after the detour rather than inferring consent from a vague “okay.”
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Follow-up after reconnect
When a transport disconnects, restore durable task state first, then restore only the conversation context your product is authorized to retain. Tell the user what remains pending instead of replaying an entire response. If the previous assistant audio was not confirmed as played, do not mark it as heard merely because generation completed.
Tools, confirmations, and cancellation boundaries
Voice interruptions frequently occur while a tool call is pending. Define cancellation semantics for each tool:
- Read-only tool: it may finish, with its result stored for the next turn if still relevant.
- Reversible write: cancel if possible, otherwise mark the operation pending and report its actual status.
- Irreversible action: require confirmation before execution and never treat an interrupted spoken “yes” as confirmation unless the confirmation turn was clearly completed.
- Long-running operation: expose progress in task state so a follow-up can ask for status without restarting the job.
Cancellation of speech is not cancellation of a side effect. Your orchestration layer must track both independently and make the distinction visible to the model and user.
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Prompt the assistant for interruption-safe behavior
Instructions should define what to do when audio is incomplete, when the user changes direction, and when the input is unclear. Include rules such as:
- Stop yielding information when a new user turn begins.
- Do not claim that an assistant sentence was heard if playback was truncated.
- Ask a concise clarification question when the user is clearly addressing the assistant but the words are unintelligible.
- Ignore silence, background media, and speech not directed to the assistant.
- Preserve confirmed task state across interruptions and ask again only for missing or invalid fields.
- Require confirmation for sensitive or irreversible actions.
Prompting cannot repair incorrect playback accounting. The state transition and truncation logic remain necessary even with excellent instructions.
Observability and recovery paths
Log enough metadata to diagnose a bad interruption without storing more audio or personal data than your policy allows. Useful fields include session identifier, transport, response identifier, turn-detection mode, speech-start and speech-end timestamps, cancellation reason, played position at interruption, truncation result, tool status, and reconnect reason.
Build explicit recovery branches:
- Microphone denied: explain how to grant permission and provide typed input if available.
- Output device lost: stop generation if appropriate, let the user select another device, and do not mark unplayed audio as heard.
- WebRTC disconnected: obtain a fresh ephemeral credential when required, reconnect, reload task state, and state what remains pending.
- WebSocket playback desynchronized: stop playback, discard queued audio, reconcile the last known played position, and request a response from corrected history.
- VAD false activation: show that the assistant is listening, allow cancellation, and tune threshold or silence settings using a representative recording set.
Test barge-in and follow-up behavior on real clients
There are no universal VAD settings or latency figures established for every browser, microphone, language, and network. Validate the complete experience on the platforms you support.
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- Interrupt after one sentence but before a list or instruction is complete.
- Pause mid-sentence without intending to yield the turn.
- Ask a correction that changes one confirmed task field.
- Ask a follow-up referring to the last audible sentence.
- Ask about information that was still queued and never played.
- Play music or hold audio near the microphone.
- Speak to another person while the assistant is listening.
- Use unclear speech that should trigger clarification.
- Disconnect and reconnect during capture, generation, playback, and a tool call.
- Switch microphones and output devices while a session is active.
For each case, inspect both what the user heard and what the model history contains. A passing test means the assistant stops promptly, does not resurrect queued audio, does not refer to unheard content, and lets the user continue the task without restating valid context.
Quick Recap
Implementation checklist
- Use HTTPS or localhost and request browser microphone permission.
- Keep the project API key on a trusted server; issue an ephemeral client credential for the browser flow.
- Choose WebRTC for the documented browser path unless your control requirements justify WebSocket.
- Document who owns capture, playback, cancellation, buffering, and truncation.
- Select semantic VAD, server VAD, or manual boundaries deliberately.
- Stop and clear playback as soon as barge-in is detected.
- Cancel the active response and truncate output to the played portion.
- Persist confirmed application task state separately from transient conversation audio.
- Support typed clarification for exact or noisy input.
- Ignore non-directed audio and ask for clarification when direct speech is unintelligible.
- Define tool cancellation and confirmation semantics.
- Test interruptions, follow-ups, noise, reconnects, and device changes on target clients.
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