Choose between a direct speech-to-speech realtime API and a cascaded pipeline that streams speech recognition, language-model generation, and speech synthesis as separate stages. Then tune turn detection and interruption handling, and measure the complete audio path on the network and devices your users will actually use. Streaming alone does not guarantee a fast-feeling conversation.
Choose an architecture before tuning for speed
The main design choice is whether one realtime service handles speech understanding and speech generation together, or whether your application connects distinct streaming services. Neither pattern is established as universally faster: compare them against the same workload, client, network, and measurement method.
| Decision factor | Direct speech-to-speech | Cascaded streaming pipeline |
|---|---|---|
| How stages are arranged | Realtime speech-to-speech session; speech input and output are handled within a unified API interaction. OpenAI documents realtime sessions over WebRTC, WebSocket, and SIP. OpenAI Realtime API Reference | Separate streaming speech recognition, LLM generation, and speech synthesis. Deepgram’s Flux tutorial demonstrates a Flux, LLM, and Aura arrangement. Deepgram Flux voice-agent guide |
| Control and observability | Fewer independently coordinated stages, but stage-level control and instrumentation depend on the API’s exposed events and settings. | Stages are independently integrated, which can make their boundaries explicit to your application; it also means more coordination and state handling. |
| Integration considerations | Select among the transports supported by the API and suitable for your client and deployment. | Connect the audio transport, recognition, turn handling, LLM, and synthesis components. Framework tutorials from LiveKit and Pipecat show orchestrated arrangements, with framework-specific setup and state to manage. |
| What to compare in your own test | Conversational quality, interruption behavior, end-to-end and per-stage latency, operating complexity, and cost for the target workload. No like-for-like result establishes a universal fastest or cheapest choice. | |
When a direct realtime API fits
Consider a direct API when its supported transports, session behavior, and controls match your client. OpenAI documents speech-to-speech use over WebRTC, WebSocket, and SIP. Choose a transport for the deployment environment, then test delivery and playback end to end rather than assuming that fewer managed stages produce lower perceived latency. Realtime API documentation
When a cascaded pipeline fits
Choose separate stages when the architecture needs explicit recognition, generation, or synthesis components, or when you want to evaluate those stages independently. That flexibility comes with integration work: your application must coordinate streaming input, turn completion, generated text, speech output, errors, and interruptions. Deepgram’s Flux guide discusses an EndOfTurn-only approach alongside other conversational patterns; treat its latency, complexity, and cost considerations as design trade-offs, not controlled benchmark results. Flux voice-agent guide
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Set up the audio path and session in the right order
For Deepgram’s documented Voice Agent WebSocket flow, initialize the session before sending microphone audio. The service endpoint and supported authentication methods are defined in its current API reference; follow that schema rather than relying on a remembered model name or field. Voice Agent API reference
- Open the WebSocket. Authenticate using a supported token or bearer mechanism. Do not place long-lived credentials in public browser code; use a server-side connection or an appropriately scoped temporary-credential design.
- Wait for
Welcome. Do not begin audio transmission before the service signals that the connection is ready. - Send one
Settingsmessage. Specify the input and output audio formats and the intended listen, think, and speak providers using the current API schema. Voice Agent Settings - Wait for
SettingsApplied. This confirms that the configuration has been applied before audio is streamed. Voice Agent Message Flow - Stream audio continuously. Send audio in the format the configured session expects; handle the provider’s text, status, warning, and error events while receiving output.
- Play returned audio and watch for speech-start events. When
UserStartedSpeakingarrives, stop local playback promptly so the user can take the floor.
Streaming reduces the need to wait for a complete recording before processing can begin, but the perceived response still includes capture and delivery, turn-end detection, model work, return delivery, buffering, and playback. A delay at any of those points can dominate what the user experiences.
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Tune turn detection for your callers
Turn detection decides when the system treats the user as finished speaking. A short wait after speech can make responses begin sooner, but it can also interpret a brief pause as the end of a turn. Tune with real conversations that include hesitations, slow speech, background noise, and pauses—not just clean, uninterrupted test utterances.
Server VAD and semantic turn detection
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Pause-based endpointing and integrated end-of-turn detection
Deepgram’s endpointing uses a configurable pause length, while its Flux guide describes model-integrated end-of-turn detection and configurable conversational dynamics. Decide what matters more for each interaction: answering quickly, or allowing more space for pauses and unfinished thoughts. Test that choice with representative speakers and conditions. Deepgram Endpointing · Flux voice-agent guide
Make barge-in stop both generation and playback
Barge-in is incomplete if the backend stops generating but the client continues playing audio already queued for output. Connect the speech-start signal to both the service response and the local audio player:
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- Interrupt or cancel the in-flight response using the cancellation behavior supported by your API.
- Stop or clear queued client audio so the agent’s previous response does not continue over the user.
Deepgram’s Voice Agent flow instructs clients to stop playback on UserStartedSpeaking; OpenAI exposes interruption behavior through turn-detection configuration. Verify the actual audible result in the client, not only whether the server reports a cancellation. Deepgram message flow · OpenAI realtime session events
Measure latency by stage and by turn
Instrument the client and service so a slow response can be traced to a stage rather than labeled simply “voice latency.” Deepgram’s server events include a Latency Report with STT, LLM, and TTS breakdowns. Add client-side timestamps for microphone capture, frame or packet delivery, the endpoint decision, first text or audio arrival, playback start, and response completion. Deepgram Server Events
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Report the conditions alongside each result. At minimum, record:
- Geography and network conditions
- Client device and audio codec or sample rate
- Language and the provider and model versions
- Turn-detection settings and the test utterance conditions
- Whether each figure is median or tail latency, and the measurement interval it covers
Measure with the deployment you intend to ship: transport, playback path, and user network affect the result. Compare architectures using the same test conditions and the same definition of response start. The available sources do not establish a controlled cross-provider benchmark. Deepgram’s tutorial describes a demo as achieving “sub-second response times,” but does not supply a shared workload and measurement method that would justify treating that as a general deployment guarantee. Flux voice-agent guide
Use the measurements to decide what to change
Once timings are split into stages, improve the stage that is actually holding back the interaction. If the endpoint decision dominates, revisit pause thresholds and turn-taking behavior; if playback begins late after audio arrives, investigate client buffering and scheduling; if one service stage dominates, compare supported configurations or architectures under the same workload. Re-run the test after each change and check both the timing distribution and whether users are being interrupted or cut off more often.
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