Most teams focus on LLM and TTS speed to reduce voice agent latency. However, the biggest improvements come from optimizing end-of-turn detection, moving from fixed silence timeouts to intelligent models that analyze intonation and cadence, often saving 300-500ms per turn.
Prioritizing speech-to-text speed over accuracy is a false economy. An inaccurate transcript forces the user into a correction loop, adding round trips of the entire pipeline. This additional latency from user corrections obliterates any milliseconds saved by a faster, less accurate model.
Focusing on average (P50) latency is misleading because users' perception is shaped by the worst interactions, not the typical ones. A single long delay can ruin the experience. Instrumenting and optimizing for tail latency (P90, P95) at each pipeline stage is critical for creating a consistently responsive agent.
Significant latency in voice agents comes from the handoffs between disparate services (STT, LLM, TTS). Each vendor hop adds network boundaries, round-trip latency, and potential points of failure. Consolidating the pipeline or co-locating services removes these 'seams' and reduces compounded delays.