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what's ideal min_silence_duration and min_delay
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s
what's ideal min_silence_duration and min_delay
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t
Thanks for reaching out, Gurkirat. I'm an AI assistant and I'm here to help. Give me a moment while I research this topic.
Thanks — you're asking about the ideal values for 
min_silence_duration
and 
min_delay
(min_endpointing_delay). Below are clear, practical recommendations and when to use them. Quick summary • min_silence_duration (how long silence must be after speech to mark turn end) ◦ Default: 0.55 s ◦ Typical range: 0.3–0.8 s • min_delay / min_endpointing_delay (minimum wait after a confident end-of-speech before responding) ◦ Default: 0.5 s (500 ms) Recommended starting values by use case • General conversational agents ◦ `min_silence_duration`: 0.3 s ◦ `min_endpointing_delay`: 0.5 s ◦ Rationale: responsive while still avoiding many false endpoints. • Agents without a turn-detector model (fixed endpointing) ◦ `min_silence_duration`: 0.5 s ◦ `min_endpointing_delay`: 0.8 s ◦ Rationale: use a bit more silence and delay to reduce premature cutoffs when you lack confidence-driven endpointing. • Latency-sensitive scenarios (e.g., live chat where speed is critical) ◦ Lower 
min_silence_duration
toward 0.3 s and keep 
min_endpointing_delay
around 0.5 s — expect a higher chance of interruptions. • Noisy environments or speakers with long pauses (IVR, drive-thru) ◦ Increase 
min_silence_duration
up to 0.8 s and raise 
min_endpointing_delay
to 0.8–1.0 s to reduce false endings. Other notes • If you use a turn-detector model, the system can vary the delay between 
min_endpointing_delay
and a 
max_endpointing_delay
(e.g., up to 6.0 s) depending on confidence; keep 
min_endpointing_delay
at ~0.5 s. • In Node.js and similar environments, 
min_endpointing_delay
is specified in milliseconds (e.g., 
500
). • Balance responsiveness vs. interruption risk: lowering values makes the agent faster but increases chance of cutting users off; raising values reduces interruptions but feels slower. If you tell me which environment or use case you’re tuning for (IVR, drive-thru, general chat, noisy field, Node.js client, using a turn-detector model, etc.), I can recommend exact values and example configuration code you can drop into your setup. Sources: Silero VAD plugin | LiveKit Documentation | VAD and turn detection configuration guide | LiveKit | examples/drive-thru | livekit/livekit_composite | DeepWiki | Using a transformer to improve end of turn detection | LiveKit | examples/voice_agents | livekit/livekit_composite | DeepWiki, with interactive elements
s
is this correct RCA-1: EOU Is 1190ms Because VAD + Turn Detection Stack Measured: EOU avg=1189.7msConfigured (both in livekit_worker.py): - prewarm():508 — min_silence_duration=0.35 → 350ms VAD silence required - entrypoint():355 — min_delay=0.4 → 400ms minimum endpointing on top Math: 350ms + 400ms = 750ms minimum hard floor before the pipeline even starts. The remaining 440ms is STT processing + network. Fix: Reduce min_silence_duration from 0.35 → 0.20 and min_delay from 0.4 → 0.25. Combined saves 300ms per turn.
t
Thanks — this is a clear write-up. I’ll validate the math and call out a few things to double-check before you change production settings. Short answer • Your basic math and reasoning are correct: the configured VAD silence (350 ms) plus the min endpointing delay (400 ms) create a baseline delay (~750 ms) before the pipeline will begin processing the turn. The measured EOU avg ≈ 1190 ms implies ~440 ms of additional time from STT, networking, and any other processing. • Reducing 
min_silence_duration
to 0.20 and 
min_delay
to 0.25 would reduce that baseline by 300 ms (750 → 450 ms), so you should see ~300 ms lower EOU if STT/network times remain the same. A few items to verify before rolling this change • Are both values measured/expressed in the same units in your code and metrics? (You showed seconds in text but 
livekit_worker.py
may use seconds or milliseconds — Node.js uses ms, others may use s.) • Confirm there aren’t other queued delays or buffering (audio chunk windowing, prewarm logic, or batching) that add to latency beyond VAD + endpointing + STT. • Check whether your turn-detector model (if used) applies additional dynamic delays (e.g., scaling up toward a 
max_endpointing_delay
when confidence is low). If so, your runtime behavior may vary by utterance confidence. • Test for increased false endpoints or interruptions. Dropping silence thresholds increases the chance the agent will cut in when a speaker pauses briefly. Suggested test plan (low-risk) 1. Stage test: apply the new settings on a canary or staging instance. 2. Run a representative set of calls covering: ◦ Normal fluent speech ◦ Short pauses (hesitations) ◦ Noisy environments 1. Measure: ◦ EOU average and distribution (P50/P90/P99) ◦ False cutoffs (turns ended prematurely) ◦ Any change in ASR error rate (if ASR is affected by earlier chunking) 1. If false endpoints increase unacceptably, consider asymmetric change: reduce 
min_silence_duration
more conservatively (e.g., 0.25) but keep 
min_delay
at 0.3–0.4 to preserve some safety. If you want, I can: • Help pick conservative test values (e.g., 0.25 & 0.3) and suggest metrics to capture. • Suggest a one-week A/B test configuration to compare user experience vs latency. Which level of aggressiveness do you want to try first: conservative (smaller reductions) or aggressive (your proposed 0.20 / 0.25)? , with interactive elements
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