Silence-Based Turn Detection Is Breaking Voice AI, and Longer Waits Make It Worse
Voice AI systems currently rely on silence detection to determine when a user has finished speaking, triggering a response after a set pause threshold is crossed. This approach fails because people naturally pause mid-thought while speaking, causing the system to interrupt them before they finish. Raising the silence threshold to reduce interruptions introduces a new problem: longer response delays make voice interfaces feel broken, since unlike visual apps there is no loading indicator to signal the system is working. Researchers and developers argue that silence alone is too weak a signal, and that humans actually use syntax, prosody, and semantic completeness to predict when someone is done talking. A more effective architecture would use a lightweight language model to continuously estimate turn-completion probability from partial transcripts, treating silence as just one of several inputs rather than the sole trigger.
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