How Turn-to-Turn Latency Shapes Voice AI

A discussion with AssemblyAI researcher Luka Chkhetiani argues that voice AI performance should be judged by the complete conversational experience rather than transcription speed alone, since turn-to-turn latency also includes endpoint detection, interruption handling, language-model processing, and response playback. Although AssemblyAI has reduced word emission latency to about 150 milliseconds, users may still perceive delays when other stages disrupt conversational rhythm. The piece emphasizes that reliable production speech AI must behave predictably across noisy, overlapping, interrupted, and otherwise imperfect real-world audio conditions, not merely score well on controlled benchmarks. It also calls for more transparent evaluation that reveals model-specific weaknesses and trade-offs among accuracy, cost, latency, infrastructure, and integration complexity. AssemblyAI’s work on agent context, adaptive conversation windows, and speaker revision is presented as a way to improve understanding, while future voice agents may benefit from recognizing and responding to signals such as frustration, confusion, or hesitation.

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