Speaker Labeler

Category: General Content Use Case: Transcript Processing and Speaker Attribution Complexity: Beginner Output: Transcript with named speakers replacing generic labels

Overview

This AI prompt replaces generic speaker labels (Speaker A, Speaker B) with actual names or roles. Essential for making transcripts readable and professionally usable.

The Prompt

Replace speaker labels in this transcript with the correct names based on the context I provide.

SPEAKER INFORMATION:
- Speaker A is: [NAME AND ROLE]
- Speaker B is: [NAME AND ROLE]
- Speaker C is: [NAME AND ROLE]
(Add more as needed)

IF SPEAKERS AREN'T IDENTIFIED:
- Analyze speaking patterns to suggest likely roles
- Note: "Based on question patterns, Speaker A appears to be the interviewer"
- Label by role if names unknown: "Interviewer:", "Guest:", "Host:"

LABELING RULES:
1. Replace ALL instances of "Speaker A/B/C" with provided names
2. Use consistent format: "Name:" at start of each speaking turn
3. Preserve all original content exactly
4. Note any speakers who couldn't be confidently identified

OUTPUT FORMAT:
- Full transcript with corrected speaker labels
- If any speakers unclear, add note at the top explaining assumptions

TRANSCRIPT TO LABEL:
[PASTE YOUR CLEANED TRANSCRIPT HERE]

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Prompt by BrassTranscripts (brasstranscripts.com)
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How to Use This Prompt

Step 1: Clean Your Transcript First

Run through the Transcript Cleaner prompt before labeling speakers.

Step 2: Gather Speaker Information

List who participated and their roles (host, guest, interviewer, etc.).

Step 3: Fill In Speaker Details

Replace the placeholder speaker information with actual names and roles.

Step 4: Review Attribution

Check a few speaker turns to verify correct assignment.

Pro Tips for Best Results

Provide roles, not just names: “Speaker A is: Sarah Chen, VP of Marketing” helps AI understand context.

Use the AI for unknowns: If you don’t know names, ask AI to identify by speaking patterns.

Verify critical sections: Double-check attribution for quotes you’ll publish.

Note uncertain attributions: Flag any speakers the AI couldn’t confidently identify.

Example Use Cases

• Interview transcripts: Assign interviewer and interviewee names
• Podcast episodes: Label host(s) and guest(s) consistently
• Meeting transcripts: Attribute statements to specific team members
• Panel discussions: Track multiple speakers throughout

Handling Unknown Speakers

When you don’t have speaker names, provide context:

Example context:

“This is a podcast interview about machine learning. One speaker is the host who asks questions, the other is a guest expert.”

The AI can then:

• Identify who asks questions (host)
• Identify who provides expertise (guest)
• Label by role: “Host:”, “Guest:”

Expected Output Format

The AI will return:

1. Full transcript with named speakers
2. Consistent labeling format throughout
3. Attribution notes for any uncertain identifications
4. All original content preserved exactly

Related Prompts

• Transcript Cleaner: Clean up raw transcript before labeling
• Timestamp Formatter: Format timestamps for your use case
• Transcript Section Organizer: Add structure and headers
• Speaker Attribution Error Corrector: Fix misattributed sections

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Source: BrassTranscripts Transcript Processing Workflow Guide

Last Updated: January 2026 Version: 1.0 Compatibility: ChatGPT, Claude, Gemini, and all major AI chat systems