# Speech-to-Text FAQ: Accuracy, Speakers and Action Items > How does AI turn a recorded call into a transcript? Word error rate, speaker diarization, naming speakers, action items and themes, answered plainly. Source: https://learn.ynteractive.com/content/blog/speech-to-text-transcription-faq · Updated 2026-10-20 · Free from Stepthrough (https://learn.ynteractive.com) Quick answer [Speech-to-text](https://learn.ynteractive.com/content/glossary/speech-to-text) turns a recording into words in three steps: clean audio goes in, a speech recognition model writes the most likely words with a timestamp on each, and a tidy-up step adds punctuation and capitals. Then **speaker diarization** splits the transcript by voice, and AI pulls out the useful parts: a summary, [action items](https://learn.ynteractive.com/content/glossary/action-items) and themes. Every one of those is only as good as the transcript under it, and transcription errors pile up on names, numbers, dates and jargon. You follow one recorded client call from sound to action items in the free [How AI Turns Talk into Text: Transcription, Speakers and Themes, Explained](https://learn.ynteractive.com/content/how-ai-transcription-works) lesson, a short click-through explainer. Last updated October 20, 2026 [Take the free module →](https://learn.ynteractive.com/courses/voice-and-meetings-ai/2)Module 2 of the free [Turn Talk Into Work: Voice and Meetings AI](https://learn.ynteractive.com/courses/voice-and-meetings-ai) course, about 6 minutes. No account needed. By [Robert Breen](https://learn.ynteractive.com/content/about) · Published October 20, 2026 · Updated October 20, 2026 · 4 min read AI Basics · Speech-to-Text · Transcription · Meetings · FAQ The example is Lena at Fairbank HR Consulting, a made-up firm. She recorded a 30-minute call with a client, Marcus, with his OK. ## How does a recording become a transcript? 1. **Record:** clean audio in. This is the biggest factor in accuracy: one voice at a time, close to the mic, no echo and no music. A headset on a quiet call beats a laptop mic across a meeting room every time. 2. **Transcribe:** a [speech-to-text](https://learn.ynteractive.com/content/glossary/speech-to-text) model listens in short slices and writes the most likely words, each with the time it was said, so you can jump back to the audio. 3. **Tidy:** punctuation and capitals are added. Dictation tools also drop the ums and fix "no wait, I mean" corrections. ## What does a transcription error look like? In the lesson's raw transcript, Marcus says the Q4 renewal, and the transcript reads "the queue for renewal". It sounds the same and means nothing. That is typical: the errors land on the words that matter most. ## Try it free, step by step **How AI Transcription Works: Speech-to-Text, Speaker Diarization and Themes**. Follow one recorded client call from sound to transcript, catch the kind of error speech-to-text makes, name the speakers, and pull out action items and themes you can trust. You do every step yourself in a practice copy of Stepthrough, it checks your work as you go, and “Do it for me” finishes any step you get stuck on. Free, and nothing touches your real accounts. [Start the free lesson](https://learn.ynteractive.com/modules/speech-to-text-explained) [Or read the full step-by-step guide](https://learn.ynteractive.com/content/how-ai-transcription-works) ## Frequently asked questions **How is transcription accuracy measured?** As [word error rate](https://learn.ynteractive.com/content/glossary/word-error-rate): 5 wrong words in 100 is 5%. A low rate can still hide the important mistakes, because errors aren't spread evenly. They pile up on names, numbers, dates and jargon. **Can I make a transcript get my client names right?** Often. Many tools let you add [custom words](https://learn.ynteractive.com/content/glossary/custom-vocabulary), such as client and product names, to help the model. **What is speaker diarization?** [Speaker diarization](https://learn.ynteractive.com/content/glossary/speaker-diarization) splits a transcript by voice, so it reads "Speaker 1" and "Speaker 2" instead of a wall of text. The software compares the sound of each stretch of speech (pitch, tone, pace) and groups the stretches that sound alike. **Does diarization know who is speaking?** No. It knows there are two voices; it doesn't know whose they are. You rename Speaker 1 and Speaker 2 once, and every line updates. Some meeting tools fill names in from the calendar invite, so check them. **When does diarization get speakers wrong?** With crosstalk and similar voices. Two people talking at once, or two similar voices on one laptop mic, get merged or swapped. Before you quote someone, check the line against the audio. **How does a transcript become action items?** AI reads it and writes owner, task and date. The lesson's example: "Lena: send the handbook draft (PTO and remote work) to Marcus and Dana by Oct 14." The owner comes from [diarization](https://learn.ynteractive.com/content/glossary/speaker-diarization) and the date from the words, so check both. See [action items](https://learn.ynteractive.com/content/glossary/action-items) and [meeting transcript to action items](https://learn.ynteractive.com/content/blog/meeting-transcript-to-action-items). **What are themes, and when are they useful?** Patterns across many calls. Across ten client interviews, [thematic analysis](https://learn.ynteractive.com/content/glossary/thematic-analysis) groups what people said into themes, like "onboarding is slow", with how often each came up and real quotes as evidence. **Can I trust a theme in a report?** Check it first. A theme is a claim about your customers. Before it goes in a report, click two of its quotes and listen to them. Quotes are where a transcription error turns into a wrong conclusion. **What is the difference between dictation, meeting notes and live help?** Three different jobs. [Voice dictation](https://learn.ynteractive.com/content/glossary/voice-dictation), like Wispr Flow, turns what you say into clean text in any app. A notetaker or ChatGPT turns a transcript into minutes after the call. A live meeting assistant, like OLVA, listens during the call and suggests answers from your notes; you still say the words. **Do I need permission to record a call?** Ask first, every time. The lesson says to tell everyone the call is recorded and get their OK, notes that some US states require everyone on the call to agree, and that client contracts may say more. Practice with made-up recordings. If you're unsure of the rules where you work, ask whoever handles legal questions for your firm. **Where do I get a transcript in the first place?** Most meeting tools and call recorders can save one. [How to get a transcript of a meeting](https://learn.ynteractive.com/content/blog/how-to-get-a-transcript-of-a-meeting) covers the common options. **What should I do every time a transcript opens?** Name the speakers first. Then search the transcript for names, numbers and dates and fix any errors. Before you send minutes or quote someone, play back the lines you're relying on. ## Key takeaways - Clean audio is the biggest factor in accuracy. Use a headset or a close mic. - Errors pile up on names, numbers, dates and jargon: check those first. - Diarization finds the voices; you name them. - Action items need a checked owner and a checked date. - Listen to two quotes before a theme goes in a report, and always ask before you record. See what usually goes wrong in [speech-to-text mistakes](https://learn.ynteractive.com/content/blog/speech-to-text-mistakes), or [start the free lesson](https://learn.ynteractive.com/modules/speech-to-text-explained). ## Free lessons in this post - [How AI Transcription Works: Speech-to-Text, Speaker Diarization and Themes](https://learn.ynteractive.com/content/how-ai-transcription-works): Follow one recorded client call from sound to transcript, catch the kind of error speech-to-text makes, name the speakers, and pull out action items and themes you can… ## Related terms Plain-English definitions, each with an example from a free lesson. [Speech-to-text](https://learn.ynteractive.com/content/glossary/speech-to-text) · [Action items](https://learn.ynteractive.com/content/glossary/action-items) · [Speaker diarization](https://learn.ynteractive.com/content/glossary/speaker-diarization) · [Descript](https://learn.ynteractive.com/content/glossary/descript) · [Notta](https://learn.ynteractive.com/content/glossary/notta) · [Meeting transcript](https://learn.ynteractive.com/content/glossary/meeting-transcript) · [Thematic analysis](https://learn.ynteractive.com/content/glossary/thematic-analysis) · [Word error rate (WER)](https://learn.ynteractive.com/content/glossary/word-error-rate) [The full AI glossary](https://learn.ynteractive.com/content/glossary) ## Keep reading - [Speech-to-Text Mistakes: 7 Transcript Errors to Catch](https://learn.ynteractive.com/content/blog/speech-to-text-mistakes): Seven mistakes with AI transcripts: bad audio, unchecked names and numbers, unnamed speakers, swapped voices, unchecked action items and no consent. - [OLVA Client Meetings FAQ: Contexts, Follow-up, Handoff](https://learn.ynteractive.com/content/blog/olva-client-meetings-faq): Plain answers about OLVA in accounting client meetings: contexts, Follow-up questions, agenda coverage, Live insight and the preparer handoff. - [Meeting Notes to CRM with AI: 12 Questions Answered](https://learn.ynteractive.com/content/blog/automate-meeting-notes-to-crm-with-ai-faq): How to get CRM deal notes from a call transcript, why "Not stated" beats a guess, how n8n turns notes into task rows and how a note reaches a CRM. 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