Episode Details

This story was originally published on HackerNoon at: https://hackernoon.com/how-to-evaluate-stt-for-voice-agents-in-production.
Most STT benchmarks measure the wrong thing. Here's how to evaluate speech-to-text for voice agents using the metrics that actually drive production performance
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Voice agent developers are optimising for TTFB — time to first byte — but it's one of the least useful metrics in production. What actually determines how fast and reliable your agent feels is TTFS (time to final segment): the gap between a user finishing speech and a stable transcript landing in your LLM. This piece breaks down the Pipecat benchmark — currently the most credible public eval for STT in voice agents — explains semantic WER and why it beats standard word error rate for this use case, and makes the case that accuracy and latency are inseparable. A faster wrong answer is still a wrong answer.