For good turn detection in LiveKit, the recommended settings depend on your specific use case. Here are some general guidelines to help you get started: • Custom Turn Detector Model: Consider using a model like

MultilingualModel

or

EnglishModel

combined with Silero VAD for natural conversations. This setup tends to offer great responsiveness with minimal interruptions. • STT-Based Turn Detection: If you're working with AssemblyAI, try configuring

turn_detection="stt"

with tuned parameters such as

end_of_turn_confidence_threshold=0.7

, and pairing it with Silero VAD to better handle interruptions. • Realtime LLM Turn Detection: For integrations like the OpenAI Realtime API, using built-in VAD modes like

semantic_vad

can provide smarter and less interruptive turn detection. • VAD-Only Detection: This is great for language-agnostic detection—setting

min_endpointing_delay

to around 400-500ms usually works well. In most cases, combining the LiveKit Turn Detector Model with Silero VAD strikes a nice balance of naturalness, accuracy, and responsiveness. Please feel free to reach out if you’d like any help implementing these settings—I’m here to support you! Sources: OpenAI Realtime API integration guide | LiveKit Docs | AssemblyAI integration guide | LiveKit Docs | OpenAI Realtime API integration guide | LiveKit Docs | VoicePipelineAgent | LiveKit Docs | livekit-agents/livekit/agents/voice/agent_session.py