Traditional voice AI pipelines suffered from compounding latency: Speech-to-Text (STT) -> LLM processing -> Text-to-Speech (TTS). Each hop introduced 500ms-1000ms delays, making conversations feel robotic and unnatural.
Native speech-to-speech models (OpenAI Realtime API, Gemini Live) compress this entire pipeline into a single direct audio-in, audio-out transformer model. Latencies drop below 300ms — matching natural human conversation speeds.
Transport architecture matters: HTTP POST polling is unviable for realtime voice. Production applications use WebRTC or WebSocket audio streaming over UDP/TCP, paired with server-side interruption handling (barge-in) so the model stops talking as soon as the user speaks.
On the backend, Node.js or Python microservices handle function calling mid-conversation. While the voice stream is active, the model can execute tools (checking hotel room availability, placing an order, or querying a CRM) and stream the result back without dropping the audio socket.
Multimodal vision integration allows users to point their camera at a physical object or screen, streaming frame samples alongside audio for instant visual reasoning, diagnostic assistance, or document inspection.
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