I understand that working with turn_detector models can sometimes feel a bit tricky, so I’m here to help you through it! The thresholds in these models are carefully calibrated to strike a balance between responsiveness and accuracy. They aren’t inherently low; rather, they’re set as probability values to help decide when an end-of-turn (EOU) is likely. If the threshold is set lower, the system responds faster, but it might interrupt a bit too soon. To help you get the best performance and reduce those false early turn endings, here are a few friendly tips you might find useful: • Enable the Semantic EOU Model: Consider using models like

EnglishModel

or

MultilingualModel

which bring in semantic understanding. • Adjust Timing Parameters: You can increase

min_endpointing_delay

to allow for longer pauses and tweak

max_endpointing_delay

to set the maximum wait time. • Customize Thresholds: If needed, you can override the default thresholds by setting

unlikelyThreshold

in your model configuration. • Ensure Language Alignment: It’s important that your STT plugin reports the correct language so the EOU model picks the right thresholds. If you’re running into any specific issues, feel free to share some details about your current setup—like the values you’re using for

min_endpointing_delay

and

max_endpointing_delay

, and whether you’re using the semantic turn detection model. I’m here to help you get it just right! Sources: plugins/livekit/src/turn_detector/english.ts | VoicePipelineAgent | LiveKit Docs | LiveKit turn detector plugin | LiveKit Docs | livekit-plugins/livekit-plugins-turn-detector/livekit/plugins/turn_detector...