100×worker · task analysis

Can AI Transcribe Audio?

By Hendrik De Winne Last updated: August 10, 2026 Lees dit in het Nederlands

Partly Partly. AI converts audio to text quickly and does well with clear, single-speaker recordings in one language. Accuracy drops with multiple speakers, background noise, accents, or technical vocabulary. You get a fast first draft, but you still need to check names, numbers, and speaker labels before using it as a record or transcript.

How to do it

You record the conversation with a dedicated microphone or headset rather than relying on a laptop speaker picking up sound from across the room. Ask participants for consent to record and process the audio before you start, since this matters regardless of the tool you use.

You upload the audio file to a tool with a transcription feature: a meeting platform, a dictation app, or a specialized transcription service. Where possible, give the tool context in advance, such as the number of speakers, participant names, and any technical terms likely to come up. The AI then generates a first transcript, including speaker labels if that feature is available.

From there, the transcript is a draft, not a finished document. You compare it against the original recording before you send it, file it, or act on it.

Step by step

Record with a dedicated microphone or headset, not a laptop speaker picking up sound from a distance.
Get consent from participants before recording and processing the conversation.
Upload the audio file to a tool with a transcription feature (meeting platform, dictation app, or specialized service).
Give the tool context where possible: number of speakers, participant names, technical terms likely to appear.
Let the AI generate a first transcript, including speaker labels if available.
Check the transcript against the recording: verify names, numbers, technical terms, and speaker attribution before you use it.

Where it breaks down

The biggest problems are speaker separation when voices overlap, background noise (open offices, street noise, a weak microphone), and language that departs from standard speech: strong regional accents, mixed dialects, or informal speech patterns. Technical terms, abbreviations, proper names, and numbers (amounts, dates, reference numbers) are where transcripts most often go wrong, even with good audio quality.

You remain responsible for checking the result. For meeting minutes, legal records, or medical dictation, always compare the transcript against the recording before you distribute or archive it. When you record conversations with clients or patients, you also need to confirm you have consent to record and process that audio. That is a separate question from how accurate the transcription itself is.

A prompt to start with

Clean up an AI-generated transcript

Here is a raw transcript generated by an AI transcription tool from a recording about [topic]. The speakers are [list names or roles, e.g., Speaker A = project lead, Speaker B = client]. Please: 1. Fix obvious errors in names, numbers, and technical terms related to [subject area]. 2. Flag any sentence where the meaning is unclear or where you had to guess. 3. Format it as [meeting minutes / interview transcript / dictation note]. Do not invent content that isn't in the transcript. Mark anything uncertain with [UNCLEAR] instead of guessing. Transcript: [paste transcript here]

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Frequently asked questions

How accurate is AI audio transcription?

It depends heavily on conditions. A single clear speaker in a quiet room, speaking standard language, typically produces a highly usable draft. Add a second or third speaker, background noise, a strong accent, or specialized vocabulary, and accuracy drops noticeably, especially for names, numbers, and technical terms. Treat any AI transcript as a first draft that needs a check against the original audio, not as a final record you can use unread.

Can AI identify who is speaking in a recording?

Many transcription tools offer speaker labeling, but it works best with a small number of speakers and minimal overlap. When people talk over each other, interrupt frequently, or sound similar, the tool can misattribute lines or merge speakers. For anything where speaker attribution matters legally or professionally, such as depositions or formal minutes, verify the labels against the recording rather than trusting them by default.

Is it legal to use AI to transcribe a conversation?

The legality depends on consent and jurisdiction, not on the technology itself. In many places you need permission from participants to record and process a conversation, and rules differ for one-party versus all-party consent. This applies whether a human or an AI does the transcribing. Check your local rules and your organization's policy before recording client, patient, or employee conversations, and get explicit consent when required.

This article was drafted with AI assistance and editorially reviewed.