AI transcription for meetings turns a recording into text with speech-to-text, splits it by speaker with diarization, and then pulls out what you actually need: a summary, decisions and action items with owners and dates. The free course Turn Talk Into Work: Voice and Meetings AI teaches it in 9 hands-on modules, starting with how speech-to-text works and where it goes wrong, then dictation, meeting minutes, interview themes, a live call assistant and workflows that file action items for you. Every module uses made-up recordings and runs in your browser.
This page is for consultants, account managers, HR and operations people, and anyone who spends hours a week in calls and interviews. It explains how voice AI works, what each module covers and what to check before you trust a transcript.
Why use AI transcription for meetings?
Meetings are where work gets decided, and where it gets lost. Notes are patchy because the person taking them is also trying to take part. The recap goes out a day late, written from memory. Action items sit in someone's notebook, and the client hears nothing. Customer interviews are worse: the insight is in there, but nobody has hours to replay ten recordings.
Voice AI captures all of it as text, then turns the text into something you can act on. A same-day recap with owners and dates keeps a client confident. A tracker that fills itself means nothing is dropped between meetings. And speaking an email instead of typing it gives back time every single day.
How does AI transcription work?
The speech-to-text explainer follows one recorded client call through every step:
- Record. Clean audio matters more than which tool you use: one voice at a time, a mic close to each speaker, no echo.
- Transcribe. A speech-to-text model listens in short slices and writes the most likely words, each with a timestamp, then adds punctuation.
- Split by speaker. Speaker diarization groups stretches of speech that sound alike and labels them Speaker 1 and Speaker 2. It does not know names, so you add them.
- Turn it into work. A summary, action items with owners and dates, and, across many calls, thematic analysis that groups what people said into themes with quotes.
Accuracy is measured as word error rate: five wrong words in a hundred is 5%. The catch is where the errors land. They pile up on names, numbers, dates and jargon, exactly the words a recap depends on. In the explainer, "the Q4 renewal" becomes "the queue for renewal". Many tools let you add custom vocabulary such as client and product names to help.
What are the 9 modules, in order?
The course starts with your own voice and works up to whole meetings and automated follow-up.
| # | Module | The job it does | Level |
|---|---|---|---|
| 1 | How Stepthrough works | How the practice works, in two minutes | Beginner |
| 2 | How AI turns talk into text | Speech-to-text, word error rate and diarization | Beginner |
| 3 | Wispr Flow: answer emails and write prompts by voice | Dictation in any app | Beginner |
| 4 | Summarize a meeting transcript with ChatGPT | Minutes, decisions and owners | Beginner |
| 5 | Speak AI: customer interviews into themes and quotes | Thematic analysis you can check | Beginner |
| 6 | CoHear: a training session into notes and a handout | An AI notetaker in the room | Intermediate |
| 7 | OLVA: a private AI assistant on your client calls | Live help during the call | Intermediate |
| 8 | n8n: meeting notes into action items in a Google Sheet | Action items filed automatically | Intermediate |
| 9 | Meeting to action: recap, tracker rows and a kickoff deck | One meeting, three outputs | Intermediate |
Modules 2 and 3: your own voice first
After the explainer, module 3 uses voice dictation with Wispr Flow: hold one key, talk, and get clean text in Gmail and ChatGPT, with the ums removed. It is the quickest win in the course, because it works in every app you already use. The habit that matters is reading the text once before you send it.
Modules 4 to 7: meetings and interviews
Module 4 turns a meeting transcript into minutes, decisions and an action table with ChatGPT. Module 5 uses Speak AI on a made-up customer interview: read the transcript by speaker, scan keywords and tone, ask for themes with quotes, and check one quote against the audio. Module 6 records an in-person training session with CoHear on an iPhone, with the room's OK, and checks its action items against the transcript. Module 7 puts OLVA beside a video call so it can suggest replies from your client notes and fact-check a number while you talk.
Modules 8 and 9: let a workflow do the follow-up
Module 8 builds an n8n workflow that reads meeting notes, asks OpenAI for every action item with its owner and due day, and adds one row per task to a Google Sheet. Module 9 combines tools: ChatGPT writes the recap, n8n fills the tracker, and Gamma builds a short kickoff deck, all from the same notes.
What should you check in an AI transcript?
Three things, every time, before anything goes to a client:
- Names, numbers and dates. Search the transcript for them and fix errors first.
- Speakers. Name them as soon as the transcript opens, and listen to any spot where people talked over each other.
- Quotes behind claims. A theme such as "clients find pricing unclear" is a claim about your customers. Play back two of its quotes before it goes in a report.
Is it legal and safe to record meetings with AI?
Tell everyone the call is being recorded and get their OK, every time. Some US states require everyone on the call to agree, and client contracts may add their own rules. Turn off model training in your tools before you upload client recordings, and follow your firm's AI policy. Every module in this course uses made-up recordings, so you can learn the steps without any real client audio.
Key takeaways
- AI transcription is speech-to-text plus speaker diarization, followed by a summary, action items and themes.
- Clean audio is the biggest factor in accuracy, and errors cluster on names, numbers and jargon.
- Check names, dates, speakers and quotes before a transcript becomes minutes or a report.
- Ask before you record, and let a workflow file the action items so nothing is dropped.
Frequently asked questions
- What is the best AI for meeting transcription?
It depends on the job. Use dictation such as Wispr Flow for your own writing, ChatGPT or a notetaker such as CoHear to turn a meeting into minutes, Speak AI for research interviews, and a live assistant such as OLVA during calls. This course teaches one tool per job, in that order.
- What is speaker diarization?
The step that works out who spoke when. It groups speech by how it sounds and labels each voice. You add the names, and you check spots where people interrupted each other.
- How accurate is AI transcription?
On clear audio it is usually very good, but the errors that remain land on rare words: names, numbers, dates and jargon. Check those before you rely on the transcript.
- Can AI create action items from a meeting?
Yes. ChatGPT can turn a transcript into a table of tasks, owners and due dates, and the n8n modules add each one to a Google Sheet automatically. Check the owners and dates against what was said.
- Is this course free?
Yes. Every module is free and the practice runs in your browser. To repeat a module for real you need an account with that tool.