{"podcast":{"title":"Tech Stories Tech Brief By HackerNoon","slug":"tech-stories-tech-brief-by-hackernoon-6365648","podcast_index_feed_id":6365648,"rss_url":"https://feeds.transistor.fm/tech-stories-tech-brief-by-hackernoon","website_url":"https://hackernoon.com/c/tech-stories","image_url":"https://img.transistorcdn.com/P-42oHG33sV1aPenbVg2DrwaV5AQtWp46GJ4Bp_EP-s/rs:fill:0:0:1/w:1400/h:1400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzQxNDI5LzE2ODM1/ODM0NjQtYXJ0d29y/ay5qcGc.jpg","author":"HackerNoon","episode_count":100,"summary":"Learn the latest tech-stories updates in the tech world.","last_synced_at":"2026-07-30T06:17:37.489609+00:00","page_url":"https://stenobird.com/podcast/tech-stories-tech-brief-by-hackernoon-6365648"},"episode":{"title":"How to Evaluate STT for Voice Agents in Production","slug":"how-to-evaluate-stt-for-voice-agents-in-production","published_at":"2026-05-02T16:00:42+00:00","page_url":"https://stenobird.com/podcast/tech-stories-tech-brief-by-hackernoon-6365648/how-to-evaluate-stt-for-voice-agents-in-production","show_page_url":"https://stenobird.com/podcast/tech-stories-tech-brief-by-hackernoon-6365648","url":"https://share.transistor.fm/s/01d3aa84","audio_url":"https://media.transistor.fm/01d3aa84/5ef5b47c.mp3","summary":"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 Check more stories related to tech-stories at: https://hackernoon.com/c/tech-stories . You can also check exclusive content about #ai-voice-agent , #voice-agent-stt , #pipecat , #voice-ai , #conversational-ai , #ai-voice-agent-benchmarking , #stt-evaluation-metrics , #good-company , and more. This story was written by: @speechmatics . Learn more about this writer by checking @speechmatics's about page, and for more stories, please visit hackernoon.com . 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.","meta_description":"This story was originally published on HackerNoon at: https://hackernoon.com/how-to-evaluate-stt-for-voice-agents-in-production . Most STT benchmarks meas…","key_points":[],"chapters":[],"topics":[],"duration_seconds":837,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/tech-stories-tech-brief-by-hackernoon-6365648/episodes/how-to-evaluate-stt-for-voice-agents-in-production/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/tech-stories-tech-brief-by-hackernoon-6365648/how-to-evaluate-stt-for-voice-agents-in-production.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}