Endpointing / Turn Detection

Turn-taking is a longstanding speech and dialogue problem. The 2022 Voice Activity Projection work trained models to predict future voice activity and evaluated turn shifts and backchannels. LiveKit later described adding transcript semantics to endpoint decisions, and in 2026 introduced an audio-based detector using acoustic and semantic cues. These are different approaches to the same interaction problem, not evidence that a single silence threshold works everywhere.

Sources: s1, s2, s3

An early boundary makes the agent interrupt the speaker; a late one creates avoidable silence. The effect is especially noticeable when a user hesitates, corrects a number or pauses within a long request. Better timing can improve a voice interface without changing the language model. It should be considered together with response cancellation and interruption handling, while remaining a separate decision from what action the agent is authorized to perform.

Sources: s1, s2, s3

In an illustrative booking conversation, the user says “move it to Thursday” and pauses before adding “after three.” A silence-only policy may act on the incomplete request. A test set should include this pause, genuinely finished requests, acknowledgments and overlapping speech. Compare premature responses and delay after the actual turn end, then verify that any interruption stops obsolete speech and that the requested booking is confirmed before commitment.

Sources: s1, s2, s3

Noise, accents, hesitation and multi-speaker audio can shift the trade-off between delay and false boundaries. Semantic cues may help but can depend on transcription quality; audio models have their own coverage limits. Evaluate the target users and channels. A correct endpoint neither guarantees correct recognition nor replaces confirmation of a consequential action.

Sources: s1, s2, s3