What is turn-taking in voice AI?

Why turn-taking makes or breaks voice interviews

Human conversation relies on subtle timing — micro-pauses, breath intakes, 'um' fillers that signal thinking continues. Voice AI that endpoints too aggressively interrupts mid-sentence, frustrating candidates and truncating answers scored for communication competencies. Systems that wait too long feel robotic and inflate interview duration.

Turn-taking sits alongside voice AI latency: even perfect transcription feels broken if the agent responds two seconds after the candidate clearly finished, or talks over them during a thoughtful pause. Candidates interpret timing errors as 'the AI is not listening' — damaging completion rates in async screens.

Indian English speech patterns include different pause lengths and filler words than US training corpora. Turn-taking models tuned only on American podcasts mis-endpoint on Indian candidates, compounding ASR errors with premature handoffs.

Technical components

• Voice activity detection — is someone speaking now?
• Endpoint detection — has the utterance ended?
• Barge-in — can the candidate interrupt the agent mid-prompt?
• Partial result handling — update transcript while user still speaking.
• Backchannel suppression — don't score 'mm-hmm' as full answers.

Evaluating turn-taking in hiring products

Demo with diverse internal speakers including accented English and noisy mobile environments — typical of high-volume India hiring. Measure completion rate and candidate satisfaction, not just demo polish in a quiet office.

Interview engines should log endpoint events for debugging — when candidates repeat themselves because they were cut off, scores on clarity unfairly drop. Turn-taking quality is an interview integrity issue, not merely UX polish.

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