There are two practical ways to build a streaming voice agent: use a direct speech-to-speech realtime API, or connect streaming speech recognition, a language model, and speech synthesis as separate stages. Neither architecture guarantees a fast-feeling conversation. Turn detection, network transit, buffering, model work, and playback all contribute, so choose a design for your client and workload, then measure the complete experience.
Choose an architecture before choosing an API
The main decision is whether one realtime service handles speech understanding and generation, or whether your application coordinates separate stages. Compare them against the same workload: transport and client support, control over turn-taking, stage-level visibility, integration effort, response quality, latency, and cost.
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| Approach | What you connect | Control and visibility | What to evaluate |
|---|---|---|---|
| Direct realtime speech-to-speech | A realtime session that handles speech input and speech output. OpenAI documents realtime multimodal sessions over WebRTC, WebSocket, and SIP, including speech-to-speech use. OpenAI Realtime API Reference | Fewer independently managed pipeline stages; turn-detection and interruption behavior still need deliberate configuration. | Which transport works for your client and deployment, how it behaves over your network, and whether the session’s controls and observability fit your product. The documentation does not establish a universal latency advantage over a cascaded design. |
| Cascaded streaming pipeline | Streaming speech-to-text (STT), a language model (LLM), then text-to-speech (TTS). Deepgram’s Flux tutorial demonstrates a Flux, LLM, and Aura arrangement. Deepgram: Build a Flux-enabled Voice Agent | Separate stages make the pipeline explicit, but your application or framework must coordinate their state and handoffs. | How partial or final transcripts trigger generation, how you handle end-of-turn, and what each stage contributes to the user’s wait. The tutorial discusses an EndOfTurn-only pattern and trade-offs of other patterns; those are design considerations, not a controlled benchmark. |
Frameworks can coordinate a cascaded design. LiveKit and Pipecat have documented Deepgram integration examples, but a framework also brings its own setup and state-handling choices: LiveKit integration and Pipecat integration.
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When a direct realtime session fits
Consider this pattern when you want a unified speech interaction and would rather manage fewer separate services. Select a transport that suits the client and deployment environment, then test audio delivery and playback end to end. The fact that a session is realtime does not establish that it will be faster than a carefully implemented cascade for your particular workload.
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When separate stages fit
Consider a cascade when the ability to reason about or configure recognition, generation, and synthesis as distinct stages matters to your design. Its extra boundaries also mean extra coordination: you need to decide what transcript or turn state starts the LLM, how generated text reaches synthesis, and how the stages respond to interruption. Measure quality, latency, and cost for the target workload rather than assuming one approach wins.
Plan transport and credentials for the actual client
Transport choice is part of the architecture, not a shortcut to low latency. Realtime sessions may use WebRTC, WebSocket, or SIP where supported; the best fit depends on the client and deployment environment. Test the whole path, including network behavior and audio playback, rather than timing only a provider-side model response. For a WebSocket implementation, follow the selected provider’s current endpoint, authentication, event, and audio-format requirements. Deepgram documents its Voice Agent endpoint and supported authentication mechanisms in its Voice Agent API reference.
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Do not put a long-lived provider credential in public browser code. Use a server-side connection or a suitably scoped temporary-credential design. Exact credential flows and supported fields are provider-specific, so verify them in the current API documentation before implementation.
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Initialize a Deepgram Voice Agent WebSocket in order
For Deepgram’s documented Voice Agent flow, do not send audio as soon as the socket opens. The initialization handshake has explicit readiness events; wait for them before advancing.
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- Open a WebSocket connection to the documented Voice Agent endpoint and authenticate using a supported token or bearer mechanism.
- Wait for the
Welcomeevent. - Send one
Settingsmessage specifying input and output audio formats and the desired listen, think, and speak providers. - Wait for
SettingsApplied. Begin streaming only after receiving it. - Stream binary PCM audio continuously, following the selected API version’s format requirements.
- Handle returned text and status events, play returned audio, and respond to errors and warnings.
- When
UserStartedSpeakingarrives, stop current playback promptly so the user can interrupt.
The message names and sequence above follow Deepgram’s Voice Agent Message Flow and Voice Agent Settings documentation. Check those references for the current schema and supported fields; provider model names and configuration fields can change. The documented flow specifies binary PCM streaming, but the required formats are configuration-dependent rather than one universal format.
Tune turn detection for the way people speak
Turn detection determines when the agent treats a user as finished. Faster endpointing can shorten the wait before a reply, but can also treat a brief pause as the end of a thought. A longer wait can preserve a hesitant or incomplete utterance while making a response feel slower.
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| Turn-detection approach | How it decides | Design consideration |
|---|---|---|
| OpenAI Server VAD | Uses detected speech and silence, with configurable threshold and silence-duration settings. OpenAI Realtime session client events | Shortening the silence duration can make responses start sooner, but may trigger during a short pause. |
| OpenAI Semantic VAD | Estimates whether the user has finished and can wait longer when speech seems incomplete. OpenAI Realtime session client events | It can have higher latency than Server VAD; choose based on the conversational behavior you need. |
| Pause-based endpointing | Uses a configurable pause length to detect an endpoint. Deepgram documents this behavior in its Endpointing guide. | Test pause settings against the speaking pace and interaction style of your users. |
| Flux end-of-turn detection | Deepgram’s Flux guide describes model-integrated end-of-turn detection and configurable conversational dynamics. Deepgram Flux-enabled Voice Agent guide | Evaluate the resulting latency and turn-taking behavior in your own interaction patterns; the guide’s trade-offs are not a cross-provider benchmark. |
Test with real conversational variation: short pauses, hesitations, background noise, and people who speak slowly. A single clean, uninterrupted prompt does not reveal whether an agent will cut off a pause or wait too long in ordinary use.
Make barge-in work in both the client and the agent
Barge-in means the user can start speaking while the agent is responding. It is incomplete if you cancel server generation but let already-buffered audio keep playing. Connect a speech-start signal to both sides of the interaction:
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- Agent response: cancel or interrupt the in-flight response where the API supports it.
- Client playback: stop playback and clear audio already queued locally, so stale speech does not continue over the user.
Deepgram explicitly instructs clients to stop playback on UserStartedSpeaking in its message-flow guide. OpenAI exposes interruption behavior through turn-detection configuration in its session client-event documentation. Confirm the exact cancellation behavior for the API and client you implement; stopping local playback and cancelling remote work are separate actions.
Measure perceived latency by turn and by stage
Streaming lets work begin before an entire utterance is complete, but it does not remove endpointing delay, model processing, network transit, buffering, or playback startup. Track the user’s wait from the end of their speech to audible agent output, and instrument the events that explain that interval. Deepgram’s server-event documentation describes a Latency Report with STT, LLM, and TTS breakdowns; add client timings to see what happens outside the server. Deepgram Server Events
- Microphone capture start and the speech-end time you use as the turn boundary.
- Audio packet or frame delivery, including when audio reaches the service.
- The endpoint decision and the time it occurs.
- First text or audio arrival from the service.
- Playback start and response completion.
- Server-side STT, LLM, and TTS latency where the provider reports it.
Use consistent event definitions when comparing turns. For example, distinguish first audio received from first audio actually played: client buffering and playback startup can make them different. Review individual turns as well as aggregate results so a slow stage or a long tail is not hidden by a single average.
Keep the test conditions with every result
Record geography, network, codec and sample rate, language, device, provider and model versions, turn-detection parameters, and the statistic reported (such as median or tail latency). These conditions help explain whether a result applies to your deployment and make later comparisons meaningful. Re-test after changing any of those factors or updating a provider configuration.
Deepgram’s Flux tutorial describes “sub-second response times” for its demo, but does not supply a shared workload and measurement method that would make that a general deployment promise. The cited documentation does not establish a controlled, cross-provider benchmark. Treat latency as an outcome to measure in your own setup, not a property guaranteed by the word “streaming.”
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