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BY COPPER SUN
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•15 min read•BrassTranscripts Team

Who Said What? How to Get Speaker Names in Transcripts [2025]

Transcribe Audio To TextSpeaker IdentificationAudio Transcription

You've transcribed your audio file, but now you're staring at a wall of text with labels like "Speaker 0," "Speaker 1," and "Speaker 2." You need to know who said what - but your transcript doesn't show which speaker is which person.

This is one of the most common frustrations with automated transcription. This guide shows you exactly how to identify speakers and get real names in your transcripts.

Quick Navigation

Why Transcripts Show "Speaker 0" Instead of Real Names

The Technical Reason

Most transcription systems work in two stages:

  1. Speech-to-text: Convert spoken words into written text
  2. Speaker diarization: Detect different voices and separate them

The AI can detect that multiple people are speaking and separate them by voice characteristics (pitch, tone, cadence). But it cannot know who those people are without additional information.

What the AI knows:

  • Voice A sounds different from Voice B
  • Voice A spoke from 0:00-0:15
  • Voice B spoke from 0:16-0:30

What the AI doesn't know:

  • Voice A is "Sarah Martinez"
  • Voice B is "John Chen"

This is why you see generic labels like "Speaker 0" and "Speaker 1" instead of actual names.

Why This is Actually Good News

The AI has already done the hard work: separating different speakers. You just need to assign names to those labels.

This is much easier than:

  • Listening to the entire recording yourself
  • Manually typing everything out
  • Figuring out who spoke when

Time saved: What would take 4-6 hours manually takes 5-10 minutes with proper tools.

Solution 1: AI-Assisted Speaker Identification (Fastest)

How AI Can Help Identify Speakers

While the transcription AI doesn't know speaker names, a different AI can analyze the transcript content to help identify who's who.

How it works:

  1. AI reads the entire transcript
  2. Looks for context clues (how people address each other, speaking patterns, topics)
  3. Suggests which "Speaker 0/1/2" corresponds to which person
  4. You review and confirm the suggestions

Using ChatGPT or Claude to Identify Speakers

Step 1: Prepare Your Information

You'll need:

  • Your transcript (with "Speaker 0, 1, 2" labels)
  • List of participants' names
  • Context (meeting type, roles, topics discussed)

Step 2: Use This AI Prompt

See our Speaker Identification Complete Guide for a detailed AI prompt that analyzes transcript context to identify speakers.

Quick version:

📋 Copy & Paste This Prompt

I have a transcript from a [meeting type] with [list names].
The transcript uses generic labels "Speaker 0," "Speaker 1," etc.

Please analyze the conversation and tell me which speaker label
corresponds to which person based on:
- How they address each other
- Topics they discuss (roles/expertise)
- Speaking patterns

Transcript:
[Paste first 500-1000 words of transcript]

Who is each speaker?

Step 3: Review AI Analysis

ChatGPT/Claude will analyze patterns like:

  • "Speaker 0 is addressed as 'Sarah' at timestamp 00:03:45"
  • "Speaker 1 discusses engineering topics, likely the Engineering Manager"
  • "Speaker 2 asks about budget, likely from Finance team"

Step 4: Verify with Audio

Listen to the first 30-60 seconds of your audio to confirm:

  • Does Speaker 0's voice sound like Sarah?
  • Does the first person speaking match who AI identified?

Step 5: Find and Replace

Once confirmed, use your text editor's find-and-replace:

  • Find: "Speaker 0"
  • Replace: "Sarah Martinez"
  • Replace all instances

Repeat for each speaker.

Success Rate

Works well when:

  • Speakers address each other by name
  • Different roles/topics are discussed
  • Transcript is reasonably long (5+ minutes)
  • You have context about participants

Struggles when:

  • No one uses names in conversation
  • All speakers have similar roles/expertise
  • Very short transcript (under 2 minutes)
  • Highly technical jargon with no context

Estimated time: 5-15 minutes for typical meeting

Solution 2: Manual Listening and Identification

When Manual is Necessary

Sometimes you need to listen to the audio yourself:

  • AI context analysis is inconclusive
  • Critical accuracy is required (legal, compliance)
  • Short recording (5 minutes or less)
  • You already know all participants' voices well

The Efficient Manual Process

Step 1: Open Audio and Transcript Side-by-Side

Use two windows:

  • Window 1: Audio player (VLC, QuickTime, or web browser)
  • Window 2: Transcript document

Step 2: Listen to First 60 Seconds

Focus on identifying voices, not content:

  • First voice you hear = "Speaker 0" or "Speaker 1"? (check transcript timestamps)
  • Second voice = which label?
  • Note distinctive voice characteristics

Step 3: Create Your Identification Key

Write down:

Speaker 0 = Sarah (higher pitch, speaks first)
Speaker 1 = Michael (deeper voice, speaks second)
Speaker 2 = Jennifer (medium pitch, third speaker)

Step 4: Verify at Multiple Points

Jump to different timestamps to confirm:

  • 00:00 (beginning)
  • Middle of transcript
  • End of transcript

Make sure labels are consistent throughout.

Step 5: Find and Replace All

Once confirmed:

  • Find: "Speaker 0" → Replace: "Sarah"
  • Find: "Speaker 1" → Replace: "Michael"
  • Find: "Speaker 2" → Replace: "Jennifer"

Tips for Distinguishing Voices

Listen for:

  • Pitch: High, medium, or low voice
  • Speaking rate: Fast or slow talker
  • Accent or dialect: Regional characteristics
  • Speech patterns: Uses certain phrases frequently
  • Volume: Consistently louder or softer

Visual cues in transcript:

  • Who speaks first/most often?
  • Who uses certain vocabulary (technical terms, specific phrases)?
  • Who asks vs answers questions?

Time required: 10-20 minutes for typical 30-60 minute recording

Solution 3: Prevention - Record with Name Tags

Best Practice: Identify Speakers at Recording Start

The easiest solution is to prevent the problem entirely.

At the beginning of every recording, have each person state their name:

Example:

Host: "Let's go around and introduce ourselves for the recording."
Person 1: "Hi, this is Sarah Martinez from Product."
Person 2: "Michael Chen, Engineering Manager."
Person 3: "Jennifer Lopez, Marketing Director."

Why this works:

  1. You hear each person's voice clearly
  2. They state their own name
  3. Early in recording = easy to reference later
  4. Takes only 30 seconds

Implementation for Different Scenarios

Team meetings:

  • "Before we start, let's identify ourselves for the transcript. I'm [name], and I'll ask each person to introduce themselves."

Interviews:

  • Interviewer: "This is [your name], and I'm speaking with [guest name]. [Guest], can you confirm that for the recording?"
  • Guest: "Yes, this is [guest name]."

Podcasts:

  • Standard intro covering all hosts and guests
  • Already common practice in podcasting

Conference calls:

  • "For our transcript, please state your name before speaking the first time."
  • "When you first speak, please say 'This is [name]' so we can identify speakers later."

Time investment: 30 seconds at recording start saves 10-20 minutes of identification work later.

Solution 4: Use Transcription Services with Speaker Tracking

Live Transcription with Participant Names

Some platforms can track speaker names during live meetings:

Otter.ai (Live Meetings)

How it works:

  • Participants join Otter meeting or connect Zoom/Google Meet
  • Each participant identified by login name
  • Transcript shows actual names instead of "Speaker 0/1/2"

Limitations:

  • Only works for live meetings (not uploaded audio files)
  • Requires all participants to have Otter accounts or meeting integration
  • Subscription required ($16.99+/month)

Zoom, Google Meet, Microsoft Teams (Live Only)

Built-in transcription shows participant names when:

  • Participants join with their real names displayed
  • Using platform's native transcription feature
  • Recording during live meeting

Limitations:

  • Only works during live meeting
  • Uploaded recordings lose participant identification
  • Accuracy varies by platform

Why Uploaded Files Lose Speaker Names

When you upload a pre-recorded audio file:

  • The transcription service only has the audio (no participant list)
  • No login names or account information
  • No way to know who the voices belong to

This is true for all services:

  • BrassTranscripts
  • Otter.ai
  • Rev.com
  • Descript
  • Any other upload-based service

The audio file itself doesn't contain identity information - only voice characteristics.

Common Scenarios and How to Handle Them

Scenario 1: Business Meeting with Known Participants

You have: Recording of team meeting, you know all participants

Best approach:

  1. Use AI-assisted identification (Solution 1)
  2. Provide AI with participant names and roles
  3. Verify by listening to first minute
  4. Find-and-replace to assign names

Time required: 5-10 minutes

Scenario 2: Interview with Two People

You have: Interview recording, you know who interviewed and who was interviewed

Best approach:

  1. Listen to first 30 seconds
  2. Identify which speaker label is interviewer vs guest
  3. Find-and-replace both labels

Time required: 2-5 minutes

Even simpler:

  • Interviewer typically speaks first (introduces guest)
  • First speaker label = interviewer
  • Second speaker label = guest

Scenario 3: Podcast with Multiple Hosts and Guest

You have: Podcast recording, multiple voices

Best approach:

  1. Use AI-assisted identification - provide names and roles
  2. Reference podcast intro (hosts usually introduce themselves)
  3. Verify identification
  4. Find-and-replace

Time required: 10-15 minutes

Scenario 4: Group Discussion with Unknown Participants

You have: Recording but don't know who's speaking

Reality: This is the hardest scenario. Without knowing participants, you can only:

  1. Keep generic labels ("Speaker 0, 1, 2")
  2. Listen to audio to learn voices, then describe them ("Male Speaker, Female Speaker")
  3. Describe by role if mentioned ("Manager, Engineer, Designer")

Workaround:

  • If participants mention each other's names in conversation, use AI analysis to catch these references
  • Descriptive labels: "Speaker A (senior person)," "Speaker B (asking questions)"

Scenario 5: Lecture or Presentation (One Main Speaker)

You have: Single main speaker with occasional questions from audience

Best approach:

  • Main speaker (most text) = Presenter name
  • Other speakers = "Audience Member" or "Q&A Participant"

Find-and-replace:

  • Speaker who speaks most = [Presenter Name]
  • All other speakers = "Audience Member" (or keep generic if Q&A isn't important)

When You Can't Tell Speakers Apart

Similar-Sounding Voices

Sometimes voices are too similar for AI or even humans to distinguish reliably.

Signs of this problem:

  • Speaker labels switch randomly mid-conversation
  • Same person appears to have multiple labels
  • You can't distinguish voices even when listening

Solutions:

1. Accept limitations and use context

  • Label by content: "Technical Discussion," "Budget Discussion"
  • Label by role: "Engineer 1," "Engineer 2"

2. Use descriptive labels based on speech patterns

  • "Speaker A (asks questions)"
  • "Speaker B (provides answers)"

3. Split by topic instead of speaker

  • Organize transcript by discussion topics rather than who spoke

4. Improve future recordings

  • Use separate microphones per speaker
  • Record in higher quality
  • Request speakers identify themselves when first speaking

Overlapping Speech

When multiple people talk simultaneously:

  • Transcription quality degrades
  • Speaker identification becomes unreliable
  • Words may be missing or incorrect

What to do:

  • Mark sections with overlapping speech: "[Multiple speakers - unclear]"
  • Listen carefully to audio and manually transcribe if critical
  • Accept that perfect accuracy isn't possible for overlapping speech

Frequently Asked Questions

Can AI automatically assign real names to speakers?

Not from audio alone. AI can:

  • ✅ Detect different voices and separate them
  • ✅ Label separated voices as "Speaker 0, 1, 2"
  • ❌ Know who those voices belong to without additional information

To get real names, you must either:

  • Provide participant names and use AI analysis to match voices to names
  • Listen to audio yourself and identify speakers
  • Have speakers introduce themselves at the recording start

How long does it take to identify speakers in a transcript?

AI-assisted method: 5-15 minutes Manual listening method: 10-20 minutes Prevention (names at recording start): 2-5 minutes post-transcription

Factors affecting time:

  • Number of speakers (more = longer)
  • How distinct the voices are
  • Whether speakers use names in conversation
  • Your familiarity with participants' voices

What if the transcript shows "Speaker 0" for everyone?

This means the transcription service did not perform speaker diarization (speaker separation).

Possible causes:

  • Free/basic service tier without speaker identification features
  • Audio quality too poor for speaker separation
  • Technical error during processing

Solutions:

  1. Use a transcription service with speaker diarization (like BrassTranscripts)
  2. Manually separate speakers by listening and editing
  3. Improve audio quality and re-transcribe

Can I transcribe a recording where I don't know who the speakers are?

Yes, you'll get a transcript, but speaker labels will remain generic:

  • Speaker 0
  • Speaker 1
  • Speaker 2

To add real names, you must:

  • Obtain participant list from meeting organizer
  • Listen to audio and identify voices yourself
  • Ask participants to identify themselves if possible

Alternative: Use descriptive labels based on context:

  • "Meeting Organizer"
  • "Engineering Representative"
  • "Client Representative"

Do video files help with speaker identification?

Most transcription services only process the audio track from video files. Visual information (faces, name tags) is not analyzed.

Exception: Some specialized systems (not widely available) can use facial recognition to identify speakers in video, but these are:

  • Expensive
  • Require clear video of faces
  • Raise privacy concerns
  • Not commonly used for general transcription

For standard transcription purposes, video and audio-only files produce identical results regarding speaker identification.

Why do speaker labels sometimes switch mid-conversation?

Common causes:

1. Audio quality drop

  • Background noise increases
  • Speaker moves away from microphone
  • Voice characteristics change (coughing, speaking loudly)

2. Overlapping speech

  • Multiple people talk simultaneously
  • AI gets confused about voice boundaries

3. Similar voices

  • Two speakers sound alike
  • AI struggles to distinguish consistently

4. Technical limitations

  • Speaker diarization isn't perfect
  • Edge cases cause errors

Solution: Manually correct these sections by listening to the audio and fixing labels.

Can I use find-and-replace to change speaker labels?

Yes, this is the standard method:

  1. Identify which generic label corresponds to which person
  2. Use text editor find-and-replace feature
  3. Replace all instances

Example:

  • Find: "Speaker 0"
  • Replace: "Sarah Martinez"
  • Replace all

Be careful:

  • Make sure you've correctly identified speakers first
  • Replacing all instances means one mistake affects the entire transcript
  • Verify accuracy by checking multiple points in transcript

What if my transcript doesn't show who is speaking at all?

You have a basic transcript without speaker separation. To add speaker identification:

Option 1: Use a transcription service with speaker diarization

  • Re-transcribe with a service that separates speakers
  • BrassTranscripts, Otter.ai, Rev, Descript, etc.

Option 2: Manually add speaker labels

  • Listen to audio
  • Add labels yourself: "[Sarah]: Hello everyone..."
  • Extremely time-consuming

Time comparison:

  • Re-transcribe with speaker diarization: 5-10 minutes
  • Manual labeling: 4-8 hours for 1 hour of audio

Re-transcribing is almost always faster and more accurate.

Conclusion

Getting real speaker names in your transcripts requires one extra step after automatic transcription, but it doesn't have to be time-consuming.

Key takeaways:

  1. AI separates voices but doesn't know names - this is why you see "Speaker 0, 1, 2"
  2. AI-assisted identification is fastest - use ChatGPT/Claude to analyze context and suggest matches (5-15 minutes)
  3. Manual listening works for simple cases - listen to first minute, identify speakers, find-and-replace (10-20 minutes)
  4. Prevention is best - have speakers introduce themselves at recording start (30 seconds)
  5. Live transcription can track names - but only during actual meetings with participant logins

Fastest workflow:

  1. Record with introductions - "For the transcript, I'm Sarah, and I'm joined by Michael and Jennifer"
  2. Transcribe with speaker diarization - use BrassTranscripts or similar service
  3. Use AI to match labels to names - provide transcript + participant list to ChatGPT/Claude
  4. Find-and-replace - swap generic labels for real names
  5. Verify accuracy - spot-check a few sections

Total time investment: 10-20 minutes for typical 30-60 minute meeting

Instead of staring at "Speaker 0" and wondering who said what, you'll have a professional transcript with actual names in under 20 minutes.

Next steps:

  1. Transcribe your recording with speaker separation enabled
  2. Try the AI-assisted identification method for your next transcript
  3. Implement "introductions at start" practice for future recordings

For professional transcription with automatic speaker separation, visit BrassTranscripts - upload your file and see exactly how many speakers are detected in your free 30-word preview.

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