are you still there

Yes — I’m here. Do you want to continue on the “discard audio unless it’s a true interruption” approach, or would you like a runnable code example (Python or Node.js) to integrate into your session? You can also paste the relevant snippet and I’ll show the minimal changes. Which would help most right now?

i got a question. is EOU delay different than endpointing delay. and is EOU something we can set

Great question — they’re related but not the same. Here’s a concise, practical breakdown. What EOU delay is • EOU (End Of Utterance) delay is the observed time between when the user actually stops speaking and when the system finalizes that utterance. It’s a measured outcome (a metric) that tells you how long turn detection took to decide the user finished. What endpointing delay is • Endpointing delay is a configurable control used by the turn-detection system. It’s the wait time the system uses after detecting potential silence before it treats the user’s turn as complete. • Common settings: ◦ min_endpointing_delay (example default ~0.5s) — the minimum wait after a confident end. ◦ max_endpointing_delay (example default ~6.0s) — the maximum wait when confidence is low. • The system chooses a delay (within the min/max) based on VAD/turn-detector confidence. How they relate • Endpointing delay influences EOU delay. If you shorten min/max endpointing delays, you’ll generally reduce observed EOU delay; increasing them usually raises EOU delay. • EOU delay is what you measure after the system applies endpointing and other heuristics; endpointing delay is one of the knobs you can turn to affect that measurement. Can you set EOU delay directly? • No — EOU delay itself is not a direct configuration parameter. You can’t set “EOU delay = X” directly. • Instead, tune endpointing parameters (min/max endpointing delay, and if used, turn-detector model behavior). These controls indirectly determine the effective EOU delay. Practical advice • If your users need faster turn finalization, lower

min_endpointing_delay

(careful: set too low and you may cut off speakers). • If you want the system to wait more when confidence is low (to avoid premature endpointing), raise

max_endpointing_delay

. • Use metrics (e.g., reported

end_of_utterance_delay

) to observe effects after each change and tune iteratively. • If you use a turn-detection model, its confidence outputs will affect which delay within the min/max range is chosen — improving model quality can reduce EOU delays without sacrificing accuracy. If you’d like, I can: • Show the exact session/turn-detector config fields to change in your SDK (Python or Node.js). • Recommend starting numeric values to test (e.g., min 0.3–0.5s, max 3–6s) tailored to phone vs. headset scenarios. Which would help you most? Sources: STT Metrics | LiveKit Documentation | VAD and turn detection configuration guide | LiveKit | Turns overview | LiveKit Documentation | Agents Framework | livekit/livekit_composite | DeepWiki

how does it work when our turn detection is owned by the STT

Yes — when turn detection is owned by the STT, the STT provider drives the end-of-turn decision rather than a separate VAD/turn-detector model. Here’s how that works in practice and what it means for your setup. How STT-owned turn detection works • The STT provider (e.g., AssemblyAI, Deepgram) performs phrase endpointing using acoustic + linguistic cues (pauses, intonation, sentence completion). When it decides a user finished, it emits an end-of-speech signal that LiveKit treats as the user’s turn end. • LiveKit receives that STT endpointing event and begins the agent’s processing (LLM, TTS) as if the turn has finished. Why this can be better • STT endpointing uses language-level context in addition to silence, so it’s less likely to prematurely finalize an utterance (fewer false endpoints). • It produces more natural turn-taking for long or syntactically incomplete utterances. What still matters (and what you should keep) • VAD is still useful: include a VAD plugin (e.g., Silero) even when using STT-owned detection. VAD detects speech onset very quickly and helps with responsive interruptions (so the agent can be interrupted promptly while speaking). • Endpointing parameters: you don’t set EOU directly, but STT providers often expose endpointing-related parameters (confidence thresholds, min/max silence) you can tune. LiveKit also uses min/max endpointing delays to bound behavior when confidence is low. Practical configuration notes • To enable STT turn-detection in LiveKit, set

turn_detection="stt"

in your AgentSession and provide a compatible STT plugin: ◦ Example:

AgentSession(turn_detection="stt", stt=assemblyai.STT(), vad=silero.VAD.load())

• If the STT provider supports tunable endpointing fields (like

end_of_turn_confidence_threshold

or

max_turn_silence

), adjust them per provider docs to match your environment (phone vs. headset, noisy vs. quiet). • Continue to use VAD for rapid interruption handling; STT endpointing improves final turn decisions but can be slower for onset detection. Edge cases and behavior to watch • Latency: STT endpointing is often more accurate but can add a bit of latency versus pure VAD silence detection. • Interruptions: rely on VAD for low-latency detection of interruption starts; rely on STT endpointing for deciding when the user has completed a thought. • Compatibility: STT-based turn detection does not apply when using some multimodal realtime LLMs that consume raw audio directly—those models handle endpointing internally. If you want, I can: • Show the exact AgentSession config for your SDK (Python or Node.js). • Suggest starter endpointing parameter values for AssemblyAI or Deepgram. • Help you add VAD + STT wiring so interruptions remain responsive while endpointing is STT-driven. Which would you like me to show next? Sources: Turns overview | LiveKit Documentation | Deepgram STT | LiveKit Documentation | AssemblyAI plugin guide | LiveKit Documentation | Improving voice AI's turn detection with transformers | AssemblyAI STT | LiveKit Documentation

but the mind_endpointing_delay parameter is effectively useless when we do it through STT?

Good question — short answer: No,

min_endpointing_delay

is not completely useless with STT-based turn detection, but its impact is reduced and depends on how you’ve wired STT and turn-detection together. How it behaves when STT owns turn detection • Primary decision: The STT provider’s endpointing (phrase-completion) is the main signal that the user finished speaking. If the STT provider returns an end-of-turn event, LiveKit will generally act on that. • Role of `min_endpointing_delay`: It acts as a safety/baseline wait. When the STT signals a turn end, LiveKit can still respect a configured minimum delay before moving the conversation forward. That prevents the system from responding too aggressively to overly sensitive STT endpoints. When it’s useful • If you rely solely on STT endpointing (no separate turn-detector model):

min_endpointing_delay

functions as a fixed minimum pause after the STT says “end of speech.” It helps avoid premature responses if STT is prone to false endpoints. • If you use a turn-detector model together with STT:

min_endpointing_delay

is part of the dynamic decision process (bounded by your min/max). The turn-detector may choose a delay between min and max based on confidence, so the parameter remains meaningful. When it’s less relevant • If your STT provider is highly authoritative and you accept its endpoint immediately,

min_endpointing_delay

may have little practical effect because you’re effectively trusting STT to drive timing. • If endpointing behavior is fully controlled and tuned on the STT side (via STT provider params), the LiveKit-side min value becomes more of a safety net than the primary control. Practical guidance • If you see premature cutoffs with STT endpointing, keep a modest

min_endpointing_delay

(e.g., 0.3–0.5s) to reduce false endpoints without adding noticeable latency. • If you need adaptive behavior, combine STT endpointing with a lightweight turn-detector model so endpointing is chosen dynamically between

min

and

max

. • Tune by measuring observed EOU delays and false-end rates after each change; reduce min only if cutoffs are rare, raise it if users are often cut off. If you’d like, I can: • Suggest starting numeric values for phone vs. headset scenarios. • Show how to set

min_endpointing_delay

and

max_endpointing_delay

in your session config (Python or Node). • Help you tune STT provider endpointing parameters (if available) and show how they interact with LiveKit’s min/max settings. Which would be most helpful? Sources: VAD and turn detection configuration guide | LiveKit | Turns overview | LiveKit Documentation

wait so STT will give us a final token and then min_endpoitning_delay will act? or its based on some confidence threshold that i set?

Good question — here’s the precise behavior in plain terms. How STT endpointing and min_endpointing_delay interact • The STT provider emits an endpointing signal (often represented as a “final” transcript or an end-of-speech event) when it decides the user’s utterance has ended. • When LiveKit receives that STT end-of-speech signal, LiveKit will still respect your configured

min_endpointing_delay

as a minimum wait before treating the turn as complete and handing the transcript to the agent pipeline. ◦ In other words: STT’s final token / end-of-speech triggers the candidate end-of-turn, and then LiveKit enforces at least

min_endpointing_delay

before finalizing the turn. Where confidence thresholds come in • Many STT providers expose confidence scores or endpointing-related parameters (e.g.,

end_of_turn_confidence_threshold

, min/max silence). Those provider-side settings affect when the STT emits its final token/event. ◦ If you raise the provider’s confidence threshold, the STT will wait for stronger evidence before emitting the final event—so your effective end-of-utterance timing moves later. • If you also use a turn-detector model in LiveKit, the detector can combine STT signals and its own confidence to choose a delay between your

min_endpointing_delay

and

max_endpointing_delay

. In that case the final decision is dynamic and depends on the model’s confidence. Putting it together (summary) • STT emits a final/end-of-speech event → LiveKit treats that as a candidate end-of-turn. • LiveKit then enforces

min_endpointing_delay

(at least) before finalizing the turn. • To influence timing earlier: tune the STT provider’s endpointing/confidence parameters so it emits the final event sooner or later. • For adaptive behavior: enable a turn-detector model; LiveKit will pick a delay within your min/max bounds based on confidence. If you want, I can: • Show the exact config fields to set

min_endpointing_delay

and

max_endpointing_delay

in your SDK. • Suggest provider-side endpointing parameters for a specific STT (e.g., AssemblyAI) and recommended starting values. • Explain how this looks in logs so you can verify which component decided the turn end (STT final token vs. turn-detector chosen delay).

and min_endpointing_delay is calculated starting when?

Short answer: the

min_endpointing_delay

countdown starts when the STT provider emits its end-of-speech / final transcript signal (i.e., when STT decides the user’s utterance is finished). More detail: • In STT-owned turn detection, the STT provider is the primary signal source. When it returns a final token or an explicit end-of-speech event, LiveKit treats that as a candidate end-of-turn. • At that moment LiveKit begins enforcing the configured minimum endpointing delay (the

min_endpointing_delay

value). LiveKit will wait at least that long after the STT final/end event before finalizing the turn and starting agent processing. • Provider-side confidence and endpointing settings affect when the STT emits that final event. Raising the STT’s endpointing confidence will delay the STT final token; lowering it will make the final token earlier—both of which change when the min delay begins to run. • If you also use a turn-detector model, the detector can choose a delay between your

min

and

max

based on confidence. In that case the min is the lower bound the system will respect after the detector/STT signals. If you’d like, I can: • Show the exact fields to set

min_endpointing_delay

(and

max_endpointing_delay

) in your SDK (Python or Node). • Suggest STT-side parameters (e.g., AssemblyAI) to tune when the STT emits its final event. • Show how to identify in logs whether the STT final token or the turn-detector chose the finalization time. Which would help most? Sources: VAD and turn detection configuration guide | LiveKit | LiveKit turn detector plugin | LiveKit Documentation | Turns overview | LiveKit Documentation | Improving voice AI's turn detection with transformers