Executive Summary

  • Human conversational tolerance is 500ms. Anything slower feels like talking to a walkie-talkie.
  • Traditional REST and bulk Websocket architectures physically cannot transmit TTS/STT and LLM inference fast enough.
  • WebRTC coupled with streaming LLM tokens drastically closes the gap to human-parity speed.
Optimal Time-to-First-Byte
350msHuman Parity

The architectural benchmark for a voice AI to perceive, think, and begin speaking.

1. The Speed Bottleneck

In a standard voice pipeline, Speech-to-Text takes 200ms, LLM thought takes 800ms, and Text-to-Speech takes 400ms. If processed sequentially, the user waits 1.4 seconds before hearing a response.

Pipeline Latency Breakdown (Milliseconds)
Sequential REST (Legacy)1400
Websocket Streaming700
WebRTC + Token Streaming + Edge TTS380

2. Interruption Handling (Barge-in)

If the user interrupts the bot mid-sentence, the VAD (Voice Activity Detection) must trigger in 50ms, kill the audio stream instantly, and inject a 'user interruption' flag into the LLM context so it acknowledges it was cut off.

3. Enterprise Use Cases

WebRTC architecture enables real-time drive-thru automation, massive outbound sales infrastructure, and live multilingual translation services with delay profiles indistinguishable from analog phone networks.