Endpointing

Endpointing is the process of detecting when a speaker has finished their utterance so that a voice AI system can begin processing and responding. It answers a specific question: "Has this person stopped talking, or are they just pausing mid-sentence?" Getting this decision right is critical to natural conversational flow — cut in too early and the system interrupts the user mid-thought; wait too long and the interaction feels sluggish and unresponsive.

The distinction from voice activity detection (VAD) is worth stating precisely. VAD answers "Is speech present right now?" — a binary, frame-level classification. Endpointing answers "Has the turn ended?" — a sequential decision that requires tracking the structure of an utterance across time. A long pause mid-sentence is still part of the speaker's turn. A short silence after a complete sentence is an endpoint. VAD is a building block inside most endpointing systems, but it is not endpointing by itself.

The latency tradeoff

Every endpointing system navigates a fundamental tradeoff between two failure modes. Set the silence threshold too short — say, 200 milliseconds of silence triggers an endpoint — and the system will cut users off during natural pauses, incomplete thoughts, or moments of hesitation. This creates the frustrating experience of being interrupted mid-sentence by a voice assistant. Set the threshold too long — 800 milliseconds or more — and the system waits noticeably before responding, making every interaction feel like a lagged phone call.

The right threshold is not universal. It depends on the use case, the expected turn length, and user population. Conversational customer support interactions tend toward shorter turns and tolerate a somewhat aggressive (short) threshold. Dictation or question-answering interactions involve longer, more deliberate utterances where a conservative threshold reduces interruption errors. Many production systems implement adaptive thresholds that vary based on detected utterance length or inferred query completeness.

How modern endpointers work

Silence-based endpointing — measuring the duration of acoustic silence after detected speech — is the simplest approach and remains common in telephony systems. It is fast and interpretable but brittle: background noise can mask silence, and speakers who trail off gradually rather than stopping cleanly will produce ambiguous silence boundaries.

Modern endpointers layer a lightweight language model prediction on top of acoustic silence detection. Rather than relying solely on "has audio energy dropped," these systems also evaluate whether the transcribed text so far constitutes a semantically complete utterance. A transcript that ends with "and then I..." followed by silence is likely incomplete; a transcript ending with "cancel my order" followed by silence is likely complete. This semantic completeness signal dramatically reduces premature endpointing on incomplete utterances without requiring a long silence buffer.

In AI voice agent architectures, endpointing is typically implemented as a separate lightweight model running in parallel with the main automatic speech recognition pipeline. The ASR model streams partial transcripts; the endpointer evaluates both the acoustic signal and those partial transcripts to predict turn completion. When the endpointer fires, the full audio segment is sent for final transcription and the pipeline begins generating a response.

Endpointing and barge-in

Endpointing logic also intersects with barge-in — the ability of a user to interrupt a voice agent while it is speaking. Barge-in requires detecting that the user has started talking before the agent has finished, which is effectively the reverse of endpointing: start detection rather than end detection. Systems that handle both barge-in and endpointing well can support fluid, overlapping conversational exchanges that approach natural human dialogue timing. Systems that handle only one create asymmetric interactions where the human must adapt to the machine's pace rather than the reverse.