Why waiting longer makes voice AI worse

Short summary

Silence-based voice activity detection for turn-ending in voice AI creates a no-win tuning problem: short thresholds interrupt users mid-thought, long thresholds make the agent feel broken. The proposed alternative uses a lightweight endpointing model over partial transcripts that incorporates syntax, prosody, and semantics rather than relying solely on silence timing. Decoupling detection from commitment allows cheap cancellation when users resume, converting a hard classification into a soft one.

• •Silence-only VAD threshold tuning fails at both ends of the range
• •Better endpointing uses syntax, prosody, and semantics as signals
• •Decoupling detection from commitment enables cheap cancellation on false endpoints

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