How does dynamic end pointing work

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how does this work internally

You're asking for the internal mechanics of dynamic endpointing — I’ll explain how it works under the hood. Overview • Dynamic endpointing adapts the agent’s wait-before-finalizing-turn using an exponential moving average (EMA) of observed pause durations in the session. • It runs only in Python and operates within

EndpointingOptions

bounds:

min_delay

and

max_delay

(Python uses seconds). What is tracked • The system measures pause intervals between utterances and between turns during the conversation. • Those pause samples feed an EMA that represents the recent typical pause length for the session. How the EMA drives delay • The EMA smooths recent pause samples into a single statistic that reflects current conversational tempo. • The effective endpointing delay is adjusted toward that EMA value while always clamped between

min_delay

and

max_delay

. ◦ If EMA indicates longer pauses, the delay moves closer to

max_delay

. ◦ If EMA indicates short, rapid exchanges, the delay moves closer to

min_delay

. How it integrates with endpointing signals • Dynamic endpointing augments VAD or STT endpoint signals: instead of relying only on a fixed silence timeout, the system factors in the EMA-derived delay so the agent is less likely to cut off slow speakers or wait too long during rapid back-and-forth. • The decision to finalize a turn uses both the instantaneous endpointing signal (e.g., detected silence) and the EMA-adjusted delay threshold. Configuration and units • You set

min_delay

and

max_delay

in `EndpointingOptions`; defaults for fixed mode are

min_delay = 0.5s

and

max_delay = 3.0s

. • Remember: in Node.js equivalents these parameters use milliseconds; in Python they use seconds. Limitations / unknowns from available info • Exact EMA smoothing factor, sample window size, and precise code-level update timing are not specified. • Internal handling of interrupted or overlapped turns during adaptation is not specified. Would you like: • an example Python snippet showing where to set

EndpointingOptions

, or • recommendations for tuning `min_delay`/`max_delay` for a specific use case? Sources: LiveKit turn detector plugin | LiveKit Documentation, with interactive elements

so min delay will go below 0.5 but not above 0.5?

Good question — let me clarify.

min_delay

is the lower bound for the adaptive delay. The adaptive (dynamic) delay will never go below

min_delay

or above

max_delay

. The default

min_delay

is 0.5 seconds, but you can configure

min_delay

to a smaller value if you want shorter minimum waits. The dynamic endpointing will then adjust the effective delay only within the configured

[min_delay, max_delay]

range. , with interactive elements