What is transcription accuracy?

Transcription accuracy measures how closely a transcript matches what was said. It covers two things: the words themselves, and who said them. Speaker attribution is the half people miss, because a line filed under the wrong person still reads fine. Wispr Notetaker works on both: the words and the names attached to them.

What transcription accuracy actually measures

Accuracy of the words means the transcript says what people said, including the names, products, and acronyms your team uses. Accuracy of the speakers means each line is attached to the person who said it. A transcript can do well on the first and still be unusable because of the second.

Not every error costs the same. Missing an "um" and misspelling the name of the person you are about to email are not the same mistake, even though both are one word. The words that matter most in a meeting, names and product terms, are usually the ones a system has never heard.

A wrong word stays on its own line. A wrong name gets copied into the recap and everything after it.

How is accuracy different from diarization and a usable transcript?

The terms around this get used interchangeably, and they do not mean the same thing.

• Transcription accuracy: how closely the text matches what was actually said
• Speaker diarization: separating a recording into distinct voices, without necessarily knowing whose they are
• Speaker attribution: putting a real name on each of those voices
• A usable transcript: accurate words and correct names together, so you can write the follow-up from it

A garbled line tells you not to trust it. A line filed under the wrong name reads perfectly, so nobody checks it. Attribution is the one people ask about, because that is the error that gets someone credited with a commitment they never made.

Why do names and acronyms get missed?

Speech systems work from sound. A name a system has never encountered has no obvious spelling, so it picks the nearest common word. The same happens to an acronym that sounds like an ordinary word.

The pattern behind all of it is the same. The transcript is missing context that the humans in the meeting already had:

• The spelling of everyone's name, which was sitting in the calendar invite
• Your company's product names and internal shorthand
• Industry acronyms that sound like ordinary words
• Who is speaking, when several people share one microphone or nobody's video tile is visible
• Vendors and tools that only your team talks about

Audio quality does the rest. A Bluetooth headset in a pocket, a laptop mic across the desk, or low input volume can each drop the beginning and end of words. A sudden drop in accuracy is almost always audio-related: a different microphone selected, a new Bluetooth connection, lower input volume, or more background noise.

What a missed name costs on a real call

Take a Tuesday customer call. The customer asks how the compliance work is going, and your teammate says the audit is stalled until the Vanta review closes. The transcript comes back with "Santa" in place of the vendor, and the sentence sitting under Speaker 2 instead of your teammate's name.

Nothing about that transcript looks broken. So the recap goes out with the wrong owner on the compliance item, and a month later a search for the vendor name finds nothing. And if you hand your meeting notes to another assistant to draft the recap, one wrong label travels into everything it writes. In meeting notes a wrong word stops where it sits; a wrong name has to be corrected in the transcript before anything is written from it.

How Wispr Notetaker handles both halves

A missing name is not a guessing problem. The system just needs the spelling before the meeting starts.

Wispr Notetaker goes into your meeting with context from your calendar, Slack, Gmail, and your personal dictionary, the same one Wispr Flow uses for dictation. The terms you taught Wispr Flow months ago show up in your meeting transcripts, spelled the way you write them.

Speakers work in two stages. During the meeting you get a live transcript, with speakers shown as you and them rather than by name. After the meeting, Wispr Notetaker reviews its own work and produces a refined transcript with speakers named. It uses calendar context plus clues from the conversation, like someone being addressed by name. On a calendar-linked one-to-one, the refined transcript names both people with no setup. When it cannot confirm who spoke, the line stays as "Speaker N" rather than a guessed name. Hover the mention, click Who is this?, and type the name once. It updates everywhere.

Nothing joins the call, so the meeting looks and sounds normal. You still need to tell people you are recording, so Wispr Notetaker keeps a consent reminder on screen the whole time it records.

Wispr Notetaker is on Mac today, with Windows coming soon. You can pull your Wispr Notetaker notes and briefs into Claude, ChatGPT, or your own setup. What you dictate in Wispr Flow is never sent into your meeting notes.

When is speaker attribution least reliable?

In-person conversations, where two or more voices arrive through one shared microphone. There, people show as Speaker 1 and Speaker 2 until the refined transcript resolves them, and you may need to name someone yourself. A manual rename always wins over any automatic assignment, so a correction sticks.

Named speaker labels appear automatically once Auto-detect speaker names is on and Wispr Flow has macOS accessibility permission. Google Meet in Chrome is the easiest case. Zoom works in the native app, and needs accessibility permission for Zoom itself; Microsoft Teams and Slack huddles from the Slack desktop app need the app's interface set to English.

Judge a tool on the names your team says every week. Record your next meeting with Wispr Notetaker and read the refined transcript before you write the follow-up.

Frequently asked questions

Why does my transcript say Speaker 1 and Speaker 2?

Live transcription runs without speaker identification, so generic labels are normal while the meeting is in progress. Names land in the refined transcript after the meeting ends. Recordings captured before you turned on Auto-detect speaker names are not renamed automatically, so re-refine an older meeting if you want names applied to it.

Can I fix a speaker name after the meeting?

Yes. Hover an unassigned mention in the transcript, click Who is this?, and type the name. It applies across the whole transcript and updates the summary chips too. Manual renames take priority over any automatic assignment, so your correction will not be overwritten later.

How do I stop a transcript from misspelling internal jargon?

Add the term to your personal dictionary. Dictionary entries you add in Wispr Flow take effect right away, with no restart. Add both singular and plural forms, and add the spelling you actually write rather than how it sounds. A CSV import handles a long vocabulary list in one pass, and starring a word gives it higher priority when several entries compete.

Does accuracy drop if I switch microphones mid-meeting?

Wispr Notetaker follows your Mac's system-default microphone, so it switches almost immediately when the device changes mid-meeting, and no recording is lost. Accuracy can still change with the new device, though: a Bluetooth headset or a mic across the desk gives it less to work with than the one you started on.

Does Wispr Notetaker keep recordings of my meetings?

Your meetings are yours. Meeting audio is encrypted and kept only temporarily to create your transcript, let you resume a meeting, verify quality, and troubleshoot. After that limited period it is automatically removed. Wispr Notetaker does not create voiceprints or biometric profiles of you or anyone on your call.