Understanding transcript quality

Transcript quality depends mostly on what goes in: clear audio produces accurate text and reliable speaker separation, while noisy or overlapping speech produces guesses. Knowing where the transcript can be wrong helps you decide what to double-check.

How accurate can a transcript be?

No in-person recording is perfect, so no transcript of one is flawless. In a room, every microphone also picks up other voices, echo and background noise, and people talk over each other. Unless every participant speaks into their own microphone (as in a video call, where each person has their own audio channel), perfect audio is practically impossible, and so is a spotless transcript.

Even the best speech recognition makes mistakes:

• Clear speech: on clear recordings of one person speaking, today's best systems get about 1 to 3 words in 100 wrong in widely spoken languages such as English, German, Spanish or French.
• Group conversations: in lively group conversations recorded with room microphones, error rates are far higher. In international research challenges on exactly this task, even the best systems still get about 1 in 5 words wrong or assign them to the wrong speaker, and about 1 in 3 in the hardest recordings, such as conversations around a dinner table.
• The microphone: in one published comparison of the same meetings, a system made about twice as many errors with a single microphone on the table as with a microphone worn by each speaker (36 compared with 17 words in 100).
• The language: speech recognition learns from recorded speech, and some languages have far more of it than others. Languages with a lot of training data are recognised much better than languages with little.

What deliberAIde does about it: after each contribution, deliberAIde recognises that passage a second time, more thoroughly, and corrects the errors it finds. Some errors still remain, so check quotes, names and numbers before you publish them.

Why it's still worth it: people speak about three to four times faster than the average person types, so a note-taker has to choose what to write down, and much is lost or paraphrased. A transcript, even with some errors, records the whole discussion, not just what someone managed to note down. Together with AI-assisted analysis, we believe this captures considerably more voices, ideas and perspectives than a note-taker alone could. Speech recognition keeps improving, and we update deliberAIde as it does.

Speaker labels

deliberAIde separates voices automatically and gives each one a numbered label, such as "Speaker 1". With crosstalk or very similar voices, a segment can land under the wrong speaker. To rename a speaker, click the speaker name on any of their segments, type the real name, and press Enter — the name applies to all of that speaker's segments and to new ones as the recording continues. Real names also make the contribution breakdown in Insights readable.

Correct the text itself

You can also fix the words in a segment, not just the speaker. Hover over a segment, select Edit, change the text, and press Enter to save. Source and translated text are edited separately: if the discussion was translated, switch between Source and Translated above the transcript to correct each version on its own.

Low-confidence segments

When you upload a written transcript, the format check summarises what it could read: segments, detected speakers, timestamps. If some lines could not be interpreted with certainty, the summary flags them as Low confidence segments — review exactly those parts in the preview before you continue, and fix the source file if they matter. See Upload text transcripts for the formats that parse cleanly.

What improves quality

The biggest gains happen before recording, not after:

• Use a decent microphone placed close to the speakers — see Audio and device setup for in-person events.
• Reduce background noise and echo where you can.
• Encourage one person to speak at a time; overlap is the main cause of wrong speaker labels.

After the fact, renaming speakers and correcting the source file of an upload are the two fixes that pay off most.

Related articles

• Audio and device setup for in-person events
• Upload text transcripts
• Insights: what the analysis shows