Turn Detection for Voice Agents: VAD, Endpointing, and Model-Based Detection

Turn detection is a critical aspect of voice agent design that determines when a user has finished speaking, allowing the system to begin processing and responding. It is essential for ensuring conversations feel natural and seamless, with incorrect detection resulting in either premature interruptions or noticeable delays. Various strategies exist for turn detection, including simple silence detection, Voice Activity Detection (VAD), STT endpointing, and model-based prediction, each with its trade-offs affecting latency and accuracy. VAD classifies audio in real-time as speech or silence, while endpointing evaluates transcription data to signal utterance completion, and model-based detection predicts turn completion based on semantic meaning. Effective turn detection is foundational to minimizing latency in the STT-to-LLM-to-TTS pipeline, and LiveKit supports multiple approaches to cater to different use cases, including handling barge-in scenarios where a user interrupts the agent.

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