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Voice AI benchmarks, latency, accuracy, and what they mean

Voice AI technology is often benchmarked on two axes, how fast it responds and how accurately it understands speech. Neither number alone tells the full story, since a fast system that mishears things is just as frustrating as an accurate one that responds too slowly to feel natural.

Why latency is measured in fractions of a second

Human conversation has a natural rhythm, with response gaps typically well under a second. Voice AI systems are benchmarked against that rhythm because anything noticeably slower starts to feel like an awkward pause rather than a natural exchange.

Why speech recognition accuracy is harder to benchmark than it sounds

Accuracy on a clean studio recording is a different problem than accuracy on a real phone call with background noise, an unfamiliar accent, or someone talking over the AI. Good benchmarks account for this gap rather than reporting only best case numbers.

Why the two need to be read together

A system tuned purely for speed can sacrifice accuracy, and one tuned purely for accuracy can feel sluggish. The systems that actually feel natural in a real conversation are the ones that balance both, rather than optimizing for whichever number is easiest to report favorably.

Frequently asked

Response times in the range of a few hundred milliseconds to around a second generally feel natural, though this can vary somewhat depending on context.

Hear real voice AI latency and accuracy on a live call.

Watch a live conversation in a 15 minute walkthrough.