How to Measure Voice AI Latency: The Complete Guide
Blog post from Coval
Voice AI latency is the delay experienced between a user's completion of speech and the AI agent's response, encompassing components like speech-to-text transcription, language model inference, text-to-speech synthesis, and network transmission. Achieving a latency of under 1 second is ideal for natural conversation, while anything over 3 seconds is perceived as poor. The guide outlines how to accurately measure latency by breaking down each component's contribution to the overall delay, emphasizing the importance of measuring in production-like conditions to account for real-world variables such as geographic distribution and concurrent user load. It highlights common mistakes like only measuring average latency, not measuring component breakdowns, and suggests optimizations such as enabling streaming across processes, choosing appropriate model sizes, and using content delivery networks for reduced network latency. The text stresses the necessity of tracking latency trends over time to avoid gradual performance degradation and suggests using latency percentiles like p95 and p99 to capture the user experience, encouraging the implementation of alert systems for sustained latency changes.
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