From Gut Feel to Hard Numbers: Meet the Voice Agent Quality Index (VAQI)

Deepgram introduces the Voice-Agent Quality Index (VAQI), a 0-to-100 metric intended to quantify conversational voice-agent performance beyond standalone transcription accuracy or latency benchmarks. VAQI combines interruptions and missed response windows, each weighted at 40%, with latency weighted at 20%, using per-conversation normalization and logarithmic latency scaling to account for varying audio difficulty and extreme delays. The company evaluated providers including Deepgram, OpenAI, ElevenLabs, and Azure in repeated food-ordering simulations featuring pauses, self-corrections, background noise, and limited speaking opportunities, with synchronized audio streams and full-stack timing measurements. The results suggest that low latency alone is insufficient, as agents must also avoid talking over users and respond reliably when it is their turn; delays exceeding roughly three seconds substantially reduce perceived quality. Deepgram reports that its system achieved the highest internal VAQI score, above 70, while presenting the index as an ongoing benchmarking tool for engineering, procurement, and product improvement rather than a definitive measure of voice-agent quality.

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