Thanks — you're asking how to make STT start up faster. Good question — here are practical ways to reduce STT startup latency you can try. • Use a streaming STT API (not batch) so partial results arrive immediately instead of waiting for complete utterances. • Reduce audio frame size (WebRTC default 20ms works well) to lower capture-to-transmit latency. • Optimize VAD so it triggers promptly (well-tuned VAD adds only ~10–50ms). • Co-locate media and inference (run STT endpoints close to your media servers) to cut network round-trips. • Aim for STT partial-result latency of ~100–200ms for natural interaction. • Monitor per-component metrics (STT processing time, e2e_latency) to find the biggest bottleneck. If you want, tell me which STT provider and deployment architecture you’re using (cloud provider, on-prem, or LiveKit inference gateway) and I’ll give targeted tweaks. Sources: Sequential Pipeline Architecture for Voice Agents | LiveKit | Voice Agent Architecture: STT, LLM, and TTS Pipelines Explained | LiveKit | Live conversations with AI using ChatGPT and WebRTC | LiveKit | Build Your First AI Voice Agent in Python: Complete Tutorial | LiveKit, with interactive elements