Guide

Transcription for AI training data versus transcription for people

The same audio needs a different transcript depending on who reads it: a model learning from every filler word and false start, or a person who just wants to know what was said. Here's how the two differ, and how to decide which one you need.

  • 4 minute read
  • Updated
  • By the IndecalAI team
The short answer

Transcription for AI training data is usually verbatim, timestamped and tagged for noise and unclear audio, because a model needs to learn what real speech sounds like. Transcription for people is usually cleaned up into readable sentences. Decide which one you need by asking what happens to the transcript next.

Verbatim vs clean transcript

A verbatim transcript captures everything: every "um," every false start, every repeated word, exactly as spoken. A clean transcript smooths that out into readable sentences, the way a person would write down what someone meant to say. A model learning to recognize real speech usually needs verbatim. A person skimming a meeting summary usually wants clean.

How we answer

We ask which one you need as part of the brief, since the same audio produces two different transcripts depending on the answer.

Timestamps and speaker labels

Word-level or segment-level timestamps let a model learn exactly when each word was spoken, which matters for anything that has to sync audio and text. Speaker labels, who said what, matter the moment there's more than one voice in the recording, whether that's a call center conversation or a meeting.

How we answer

Timestamps and speaker labels are part of the standard delivery, not an extra we charge separately for.

Noise, fillers and overlapping speech

Real audio has background noise, people talking over each other, and words that get swallowed or unclear. A transcript for AI training data usually tags these rather than guessing or leaving them out, since a model needs to learn what uncertain audio actually sounds like, not a cleaned-up version of it.

How we answer

Unclear audio gets tagged, not guessed at, and overlapping speech is marked rather than merged into one line.

Formatting for a model vs a reader

A model doesn't care about paragraph breaks or punctuation for readability, it cares about consistent, structured formatting it can parse the same way every time. A person reading a transcript wants punctuation, paragraphs and a style that reads naturally. Mixing the two goals in one transcript usually serves neither well.

How we answer

We agree the format upfront, structured for a model or readable for a person, rather than defaulting to one and hoping it fits.

How to decide which one you need

Ask what happens to the transcript next. If it trains or evaluates a model, lean verbatim, timestamped and structured. If a person reads it to understand a conversation, lean clean and readable. Some projects genuinely need both: a verbatim version for training and a clean version for a human reviewer.

How we answer

Tell us what the transcript feeds into, and we'll tell you which format actually fits, rather than defaulting to one for every job.

Warning signs

Be cautious of a vendor who doesn't ask which kind of transcript you need, delivers clean text with no option for verbatim, or can't explain how they handle unclear audio and overlapping speech.

Keep reading

Where to go from here

What goes into the audio itself on the speech data page, and the questions worth asking any vendor in the vendor guide.

Transcription

Audio turned into reviewed text.

Conversational AI data

Dialogue and calls for voice products.

Data annotation and labeling

Labels checked batch by batch.

FAQ

Common questions

What people ask while scoping a transcription project. The services FAQ covers how we work.

What's the difference between verbatim and clean transcription?

Verbatim captures everything exactly as spoken, including false starts and filler words. Clean transcription smooths that into readable sentences. AI training data usually needs verbatim.

Do I need timestamps in my transcript?

If anything needs to sync audio and text, such as training a speech model, yes. If the transcript is just for a person to read, timestamps usually aren't necessary.

How is unclear or noisy audio handled in a transcript?

It should be tagged, not guessed at or silently dropped. A model needs to learn what real, imperfect audio looks like.

Can the same audio be transcribed two different ways?

Yes. Some projects need a verbatim version for training and a separate clean version for a human reviewer, from the same recording.

Scoping a transcription project?

Tell us what the transcript feeds into. Krishsinh will write back within one business day.

Talk to our team