Turn detection in voice agents: silence timeouts vs VAD vs semantic models

End-of-turn detection determines when a voice-agent caller has finished speaking and must balance false interruptions against response latency. Fixed silence timers and voice activity detectors infer completion from quiet, but timers confuse mid-sentence pauses with finished turns while VADs cannot distinguish hesitation from completion and may be affected by noise; both require configuration choices that can add delay or errors. Models that fuse acoustic cues such as prosody and timing with transcript-level semantic context can better recognize whether a thought is complete, though they introduce compute costs and confidence-threshold tuning, with a silence timeout retained as a fallback. Because no universal standard defines an acceptable false-interruption rate, platforms should establish their own baseline through stratified sampling of deployed calls, define interruptions consistently, compare controlled configuration changes on the same sample, and examine results by language, customer, and queue. Eager end-of-turn approaches can begin LLM generation 150–250 milliseconds sooner on likely completions, but increase discarded drafts and LLM calls when callers continue speaking, making them more suitable for inexpensive, short transactional interactions than dictation or costly workloads. The recommended approach is to begin with default settings, measure interruption rates and response times before tuning, test multilingual and noisy conditions, and track eager-generation waste where enabled.

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