Realtime Voice APIs: Speech-Native Agents
Instead of chaining three models (listen → transcribe → think → speak), the 2024-2026 generation of realtime APIs — OpenAI's Realtime API (WebRTC/SIP, gpt-realtime) and Google's Gemini Live — uses one audio-native model that hears tone and speaks with it, hitting conversational latency. The engineering that decides whether it feels human is turn-taking, barge-in, latency budgets, and telephony — not model IQ.
01.The Problem: Your AI Replies Like a Form Letter Read Aloud
Build a voice assistant the obvious way and you get a pipeline:
codeears (STT) ──► brain (text LLM) ──► mouth (TTS) speech ──► words ──► answer ──► speech
That chain works — but talk to it and something feels off:
- you finish a sentence and there is an awkward half-second of nothing, then another, then another,
- you interrupt and it keeps monologuing,
- you sigh, and it hears silence — the sigh, the hesitation, the tone, vanished at the first hop, because speech got flattened into plain text.
So the question that defines this topic is
What if the model never flattened your voice at all — one network hearing audio and producing audio, the way two people actually talk?
That is the bet of the realtime voice APIs — OpenAI's Realtime API and Google's Gemini Live — the shift from "AI that reads its answers aloud" to "AI that speaks."
Cascade vs speech-to-speech latency
Cascade vs speech-to-speech latency
Cascading three models multiplies latency and strips paralinguics; realtime APIs process audio tokens directly, enabling interruptions, tone, and sub-500ms responses.
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