How to Transcribe Multiple Speakers and Label Them

To transcribe audio with multiple speakers and label them, upload your recording to the audio-to-text tool, enable speaker labels, then review and rename the automatic speaker assignments before exporting.

This guide walks through the whole process: preparing the file, running the transcription with speaker labels turned on, correcting the labels by hand, and exporting a transcript you can share or publish.

Why Speaker Labels Change the Transcript

When you transcribe an interview, meeting, panel, or podcast episode, the raw text can quickly become a wall of words. Speaker labels, also called diarization, separate the conversation into turns and assign each turn to a speaker. That turns "what was said" into "who said what."

Without labels, you have to guess which person made each point, which is slow and error-prone. With labels, you can skim the structure of a conversation, search for one person's contributions, and quote people accurately. A labeled transcript also reads better for anyone who was not in the room, because questions and answers are easy to tell apart at a glance.

A good multi-speaker transcript is not just a text dump. It is a record you can hand to a teammate, turn into meeting minutes, cut quotes from, or convert into subtitles. Treat speaker labeling as a two-part job: let the tool do the rough separation, then you make it trustworthy with a final review.

Before You Start: Gather the Right Source

A multi-speaker transcription is only as good as the source audio. You need three things before you begin:

• An audio or video file on your device, or a URL to a public recording.
• A reasonably clear recording where each speaker can be heard.
• The names or roles of the speakers, if known.

That third item matters more than people expect. If you know the participants ahead of time, renaming Speaker 1 and Speaker 2 takes seconds instead of guesswork. For an interview you conducted, you already know the names. For a recorded meeting, check the calendar invite or the call's participant list.

If the recording is noisy or has heavy crosstalk, speaker separation may be less reliable. You can still transcribe it, but plan for a longer review pass. A little preparation before uploading saves time later.

How to Transcribe Multiple Speakers and Label Them

Follow this step-by-step workflow to move from a messy multi-speaker recording to a clean, labeled transcript.

1. Open the audio-to-text tool and upload your recording. You can upload an audio or video file from your device, or paste a public URL if the recording is hosted online.
2. Choose the source language if needed. CATT supports many languages, so multilingual conversations are not a blocker.
3. Turn on speaker labels or diarization. This tells CATT to separate the audio into speaker turns instead of returning one continuous block. Skipping this step is the most common mistake, because labels added afterward mean redoing work.
4. Run the transcription. Wait while the tool processes the file. You will receive a transcript with generic labels such as Speaker 1, Speaker 2, and so on. CATT also produces an AI summary and action items, which help you spot key decisions before you start editing line by line.
5. Read through the transcript once without editing. Mark any place where a label looks wrong, a speaker changes mid-sentence, or two voices appear merged into one turn. This gives you a map of the problem areas so you can fix them in order.
6. Rename the generic labels. Replace Speaker 1 with the person's actual name, Speaker 2 with the next name, and continue for every voice in the recording. A find-and-replace pass works well here, especially on longer transcripts.
7. Fix misplaced lines. If a sentence sits under the wrong speaker, move it to the correct label. If two people spoke at once, decide who owns the line or split it manually between both labels.
8. Export the labeled transcript. Choose TXT for a clean written record, SRT or VTT for captions, or another available format that fits your workflow.

Steps 5 through 7 are where the real quality comes from, so the next two sections go deeper on them.

Making Speaker Labels More Accurate

The biggest factor in automatic speaker separation is the recording itself. To improve results before you upload:

• Record each speaker on a separate microphone when possible.
• Reduce background noise and echo. Rooms with hard surfaces reflect sound and blur voice patterns together.
• Ask speakers not to interrupt each other. Clean turn-taking gives the tool clear boundaries to work with.
• If one speaker is much quieter than the others, normalize the audio or move them closer to the microphone.
• Avoid music playing under the conversation. An intro sting is fine, but a continuous bed of music makes voices harder to separate.
• Test with a short clip first. Run the first few minutes of a long recording to confirm the setup separates voices cleanly before committing to the full file.

None of these guarantees perfect separation, but each removes a common obstacle. Distinct voices, clean turns, and low noise give the tool exactly what it needs.

Reviewing and Correcting Speaker Labels

Automatic labels get the structure right most of the time, but names and edge cases need a human. Work through the transcript in three passes:

First pass: read and flag. Read the whole transcript once and note anything suspicious: turns that switch topic mid-stream, short interjections attributed to the wrong person, and stretches where the text sounds like one voice but the content suggests two.

Second pass: rename. Swap the generic labels for real names with find-and-replace. Use one name consistently throughout. If someone appears as both Sarah and Sarah K. in different spots, pick one form and stick with it, because inconsistent names break search later.

Third pass: fix the flagged lines. Return to your marked sections. Listen to the audio at each spot, then move the text under the correct label or split a shared turn in two. When two people talk over each other, decide whose words matter for your purpose and attribute the line accordingly. If a phrase is genuinely unintelligible, mark it as inaudible rather than guessing, so nobody quotes a wrong word later.

Three passes sound slower than editing as you read, but they are faster in practice. Renaming first means every later correction happens under final names, so you never redo a fix.

Choosing an Export Format

The right export depends on what happens next:

• TXT suits articles, notes, meeting minutes, and anywhere you want plain readable text with speaker names attached to each turn.
• SRT and VTT suit captions and subtitles. They carry timing information alongside the text, so each labeled turn appears on screen while the person speaks.
• Other formats fit specific tools, such as editors or subtitle platforms. Pick whichever matches the software downstream of your transcription.

If you are unsure, export TXT first for the written record, then export a caption format separately if the transcript will appear on video. Keeping both versions covers writing and publishing needs at once.

Common Problems and Quick Fixes

Heavy crosstalk. Overlapping speech is the hardest case for any diarization system. Expect a longer review pass, and lean on the audio itself when deciding who said what.

Similar-sounding voices. When two speakers sound alike, their turns may get merged. Content cues help: questions usually belong to the interviewer, answers to the guest. Verify each merged stretch against the audio.

One quiet participant. A soft-spoken speaker can get folded into the nearest loud voice. Boost their channel or re-record that portion if you still can, otherwise budget extra review time for their turns.

Long recordings. For a multi-hour file, edit in passes rather than one sitting. Use the AI summary and action items to jump to the segments that matter most, then clean those first.

Wrapping Up

Multi-speaker transcription is a two-step habit: let the tool separate the voices, then spend one focused pass making the labels true. Upload your file to the audio-to-text tool, enable speaker labels, and treat the review as part of the job rather than an optional extra. That short, careful pass at the end is the difference between a transcript you trust and one you quietly rewrite anyway.