Endpointing determines when a spoken utterance has ended so a speech recognizer can finalize the transcript or a voice system can begin its response. It usually combines voice activity, silence duration, and sometimes linguistic context. Poor endpointing either clips late words or adds an unnatural pause after every turn.
How does endpointing work?
A speech system watches audio frames and waits for evidence that the utterance is complete. Short silence thresholds reduce delay but increase the risk of clipping a natural pause. Longer thresholds protect the final words while consuming more of the latency budget. Voice activity detection supplies the acoustic boundary, while language cues can refine it.
How should an endpoint be tuned?
Tune against real utterances, including addresses, numbers, hesitations, and background noise. Report clipping and response delay together, because improving one can worsen the other. Streaming automatic speech recognition may emit partial text before the endpoint, but should mark the final result clearly. Turn detection can then decide whether the system should answer, wait, or continue listening.
Frequently asked questions
Is endpointing the same as turn detection?
Endpointing marks the end of an utterance for speech processing. Turn detection is broader and decides when the conversation should pass to another speaker.
What is endpointing latency?
It is the delay between the speaker finishing and the system deciding the utterance is complete.
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