Speaker Diarization Guide - Identify Speakers in Audio and Video

What is Speaker Diarization?

Speaker diarization is the process of automatically detecting and labeling different speakers in an audio or video recording. The term “diarization” comes from “diary” - creating a record of who spoke when.

When you transcribe a conversation, podcast, interview, or meeting with multiple people, diarization answers the critical question: “Who said what?”

Without diarization:

Welcome to today's podcast. Thanks for having me. Let's start with
your background. I started in tech 15 years ago working at...

With diarization:

[Speaker 1]: Welcome to today's podcast.
[Speaker 2]: Thanks for having me.
[Speaker 1]: Let's start with your background.
[Speaker 2]: I started in tech 15 years ago working at...

Better yet, with named speakers:

[John Smith]: Welcome to today's podcast.
[Sarah Johnson]: Thanks for having me.
[John Smith]: Let's start with your background.
[Sarah Johnson]: I started in tech 15 years ago working at...

Why Speaker Diarization Matters

Speaker identification transforms raw transcripts into organized, usable documents:

Key benefits:

• Clear attribution: Know exactly who said what
• Better comprehension: Follow conversations easily
• Easy quoting: Extract specific person’s statements
• Meeting minutes: Attribute decisions and action items
• Interview analysis: Organize Q&A by speaker
• Podcast production: Create show notes with host/guest labels
• Research: Analyze individual speaker contributions

Use cases:

• Business meetings (track who made which decision)
• Interviews (separate interviewer from interviewee)
• Podcasts (host vs guest identification)
• Focus groups (individual participant tracking)
• Legal depositions (attorney vs witness)
• Customer calls (agent vs customer)
• Conference panels (multiple speakers on stage)

How Speaker Diarization Works (The Science)

ScreenApp uses advanced AI to detect and separate speakers:

Step 1: Voice Feature Extraction

The AI analyzes audio characteristics for each segment:

• Pitch: Fundamental frequency of the voice
• Tone: Voice quality and timbre
• Cadence: Speaking rhythm and pace
• Energy: Volume and emphasis patterns
• Formants: Vocal tract resonance frequencies

These features create a unique “voice fingerprint” for each speaker.

Step 2: Speaker Clustering

The AI groups similar voice segments:

1. Analyzes voice features across the entire recording
2. Identifies distinct clusters of similar voices
3. Assigns each cluster a speaker label (Speaker 1, Speaker 2, etc.)
4. Segments are grouped by speaker based on voice similarity

How clustering works:

• AI detects voice changes (different pitch, tone, etc.)
• Similar voices across different timestamps are grouped together
• Each cluster becomes one speaker
• Clusters are numbered sequentially (Speaker 1, 2, 3…)

Step 3: Segment Assignment

Every spoken segment gets assigned to a speaker:

1. AI determines where one speaker stops and another starts
2. Each segment receives a speaker label
3. Timestamps mark when each speaker talks
4. Transcript displays organized by speaker

Accuracy factors:

• Clear, distinct voices: 90-95% accuracy
• Similar-sounding speakers: 75-85% accuracy
• Overlapping speech: 60-75% accuracy
• Background noise: Reduces accuracy by 10-20%

Step 4: AI Speaker Name Suggestions (Optional)

For certain content types, AI may suggest speaker names:

1. Analyzes conversation context
2. Looks for speaker introductions (“Hi, I’m John…”)
3. Detects role patterns (interviewer vs interviewee)
4. Suggests names based on context clues

You can accept suggestions or manually assign names.

Step-by-Step: Using Speaker Diarization

Step 1: Upload Multi-Speaker Audio/Video

1. Go to ScreenApp
2. Click “Upload” or drag and drop your file
3. Alternatively, use “Import from URL” for meeting recordings
4. Wait for upload to complete

Best content for diarization:

• ✅ Interviews (2 speakers)
• ✅ Podcasts (host + guest)
• ✅ Meetings (3-10 participants)
• ✅ Panel discussions (multiple speakers)
• ✅ Customer calls (2 speakers)
• ⚠️ Large conferences (10+ speakers - may be complex)

File requirements:

• Clear audio (minimal background noise)
• Distinct voices (different pitch/tone)
• Minimal speaker overlap
• Good microphone quality

Step 2: Automatic Transcription with Diarization

After upload:

1. ScreenApp automatically transcribes the audio
2. Status shows “Transcribing…” then “Diarizing…”
3. AI detects different speakers during transcription
4. Speaker labels assigned automatically (Speaker 1, Speaker 2, etc.)
5. Processing completes in 1-3 minutes for most recordings

What happens during diarization:

• Speech-to-text transcription
• Voice fingerprint extraction
• Speaker clustering and segmentation
• Timestamp assignment per speaker
• Optional AI name suggestions

Processing time:

• 2-speaker conversation: ~1 minute per 10 minutes of audio
• 3-5 speakers: ~1.5 minutes per 10 minutes
• 6+ speakers: ~2 minutes per 10 minutes

Step 3: Review Speaker-Labeled Transcript

Once processing completes:

1. Click your file to open it
2. Navigate to the Transcript tab
3. Each segment shows speaker label (Speaker 1, Speaker 2, etc.)
4. Speaker labels appear before each segment of dialogue

Transcript format:

Speaker 1: Welcome everyone to today's meeting.
Speaker 2: Thanks for having us.
Speaker 1: Let's start with the quarterly update.
Speaker 3: I can present the numbers first if you'd like.

Reviewing accuracy:

• Check that distinct speakers have different labels
• Verify speaker changes happen at the right timestamps
• Look for mislabeled segments (wrong speaker)
• Note if multiple speakers were grouped as one

Step 4: Assign Real Names to Speakers

Replace generic labels with actual names:

1. In the Transcript tab, find a segment from the speaker
2. Click the speaker label (e.g., “Speaker 1”)
3. A dropdown appears showing:
   • Current speaker label
   • AI-suggested names (if available)
   • Team members (if workspace connected)
   • Option to enter custom name
4. Select or type the person’s real name
5. Click to confirm

All segments from that speaker update automatically throughout the transcript.

Assigning names:

Before:
Speaker 1: Let's start with introductions.
Speaker 2: Hi, I'm Sarah from Marketing.

After naming:
John Smith: Let's start with introductions.
Sarah Johnson: Hi, I'm Sarah from Marketing.

Name assignment options:

• AI suggestions: If AI detected names from context
• Team members: Select from your workspace members
• Custom names: Type any name manually
• Clear label: Remove custom name, revert to Speaker X

Step 5: Bulk Speaker Editing (Optional)

If you need to change multiple speaker assignments:

1. Some segments may be mislabeled (Speaker 1 should be Speaker 2)
2. Click on a mislabeled segment
3. Change the speaker assignment
4. ScreenApp allows editing individual segments

When to use bulk editing:

• AI confused two similar-sounding speakers
• Multiple speakers got merged into one label
• One speaker got split into multiple labels

Editing workflow:

1. Identify patterns of mislabeling
2. Click segment with wrong speaker
3. Reassign to correct speaker
4. Repeat for other mislabeled segments

Improving Speaker Detection Accuracy

Before Recording

Optimize audio setup:

• Use quality microphones (external preferred over built-in)
• Position mics 6-12 inches from each speaker
• Reduce background noise (close windows, turn off fans)
• Use separate mics for each speaker if possible
• Test audio levels before recording

Recording environment:

• Quiet room with minimal echo
• Avoid hard surfaces (use soft furnishings to reduce reverb)
• No overlapping music or background audio
• Minimize paper rustling and keyboard typing

Speaking guidelines:

• Avoid talking over each other
• Allow brief pauses between speakers
• Speak at normal volume and pace
• Don’t whisper or shout
• Keep consistent distance from microphone

During Diarization

If diarization accuracy is low:

1. Check audio quality: Poor audio = poor speaker detection

   • Re-record with better microphone if possible
   • Use noise reduction tools before uploading
   • Ensure volume levels are adequate

2. Verify speaker count: Too many or too few speakers detected

   • If AI detects fewer speakers than actual: Voices too similar
   • If AI detects more speakers than actual: One person’s voice varied too much
   • Manual correction needed in these cases

3. Review speaker changes: Are transitions accurate?

   • Check where AI thinks speaker changed
   • Verify it matches actual speaker transitions
   • Manually correct if needed

After Diarization

Manual cleanup:

• Review entire transcript for mislabeled segments
• Focus on sections where speakers overlap
• Correct ambiguous segments where speaker unclear
• Verify names are assigned correctly throughout

Quality check:

1. Sample random segments throughout transcript
2. Ensure speaker labels match audio
3. Check that all speakers have been identified
4. Verify no speaker was split into multiple labels

Common Diarization Challenges

Challenge 1: Similar-Sounding Voices

Problem: Two speakers with similar pitch/tone get confused

Example scenarios:

• Two male speakers with similar voice characteristics
• Family members (similar genetics = similar voices)
• Speakers from same region (similar accents)

Solutions:

1. Review transcript carefully for switches
2. Use context clues (who would say what)
3. Manually reassign mislabeled segments
4. In future recordings, have speakers identify themselves periodically

Accuracy: Drops from 90-95% to 75-85% for similar voices

Challenge 2: Overlapping Speech

Problem: Multiple people talking at once

Example scenarios:

• Crosstalk in heated discussions
• Simultaneous agreement (“Yes!” from multiple people)
• Interruptions mid-sentence

Solutions:

1. AI typically assigns to the louder speaker
2. Overlapping portions may be unclear in transcript
3. Manual review needed for critical overlaps
4. In future: Establish speaking order or use raised hands

Accuracy: Drops to 60-75% during overlapping speech

Challenge 3: Single Speaker with Variable Voice

Problem: One person’s voice changes significantly

Causes:

• Emotional changes (calm to excited)
• Physical changes (standing vs sitting)
• Distance from microphone varies
• Cold or illness affecting voice
• Shouting or whispering

Solution:

1. AI may split one person into multiple speakers
2. Review and merge speaker labels if needed
3. Manually reassign segments to correct speaker

Challenge 4: Background Voices

Problem: Ambient voices detected as speakers

Example scenarios:

• Someone talks in the background
• TV or radio playing
• Nearby conversation
• Voice from phone call on speaker

Solutions:

1. AI may create extra speaker labels for background voices
2. Manually remove or ignore these segments
3. In future: Mute background audio sources during recording

Challenge 5: Phone/Video Call Audio

Problem: Compressed audio from calls reduces accuracy

Causes:

• Call compression degrades voice quality
• Network issues cause audio artifacts
• Speaker phone echo
• Low bitrate audio

Solutions:

1. Record locally if possible (not just the call audio)
2. Use high-quality call recording tools
3. Avoid speakerphone when possible
4. Ensure strong network connection
5. Accept that accuracy may be 10-15% lower for call recordings

Speaker Diarization Use Cases

1. Meeting Documentation

Workflow:

1. Record meeting (Zoom, Google Meet, Teams)
2. Upload to ScreenApp for transcription + diarization
3. Assign names to each participant
4. Export transcript with speaker labels
5. Distribute meeting minutes to team

Benefits:

• Clear attribution of who said what
• Track decisions and action items by person
• Accountability for commitments made
• Easy to extract quotes for summaries

Example output:

[John Smith - CEO]: Let's review Q4 goals.
[Sarah Johnson - CFO]: Revenue is up 15% this quarter.
[Mike Chen - CTO]: We launched 3 new features.

2. Interview Transcription

Journalist/Researcher workflow:

1. Record interview (in-person or remote)
2. Get diarized transcript
3. Assign Interviewer and Subject labels
4. Extract quotes with proper attribution
5. Use for article writing or research analysis

Benefits:

• Easy to find specific person’s statements
• Accurate quote attribution for publication
• Analyze interview patterns
• Create Q&A format transcripts

Example format:

[Interviewer]: What inspired you to start the company?
[Subject]: I saw a gap in the market for...
[Interviewer]: How did you fund the initial development?
[Subject]: We bootstrapped for the first two years...

3. Podcast Production

Podcaster workflow:

1. Record podcast episode with guests
2. Get diarized transcript
3. Assign host and guest names
4. Create show notes from transcript
5. Extract highlights for social media

Benefits:

• Auto-generate show notes with speaker attribution
• Create episode summaries easily
• Pull specific guest quotes
• Build searchable podcast archive
• Generate blog posts from episodes

Podcast show notes example:

[00:00] - John (Host) introduces episode topic
[02:15] - Sarah (Guest) shares her background
[15:30] - Discussion of main topic
[42:00] - Rapid-fire Q&A segment

4. Focus Group Analysis

Market research workflow:

1. Record focus group session
2. Diarize to separate participants
3. Assign participant IDs (Participant 1, 2, 3 for anonymity)
4. Analyze responses by participant
5. Extract themes and patterns

Benefits:

• Track individual participant contributions
• Analyze dominant vs quiet participants
• Extract specific feedback by person
• Quantify participation rates
• Identify consensus or disagreement

5. Customer Service Call Analysis

Call center workflow:

1. Record customer support calls
2. Diarize Agent vs Customer
3. Analyze call patterns
4. Extract successful resolution techniques
5. Train agents based on best practices

Benefits:

• Separate agent from customer speech automatically
• Analyze agent performance
• Identify common customer concerns
• Extract verbatim customer quotes
• Monitor call quality and compliance

Exporting Speaker-Labeled Transcripts

Download diarized transcripts in multiple formats:

Export Formats with Speaker Labels

1. Plain Text (.txt) - Simple format with speaker names

   John Smith: This is the first point.
   Sarah Johnson: I agree with that assessment.
   

2. Word Document (.docx) - Formatted with speaker names and timestamps

   • Each speaker change on new line
   • Timestamps included
   • Speaker names in bold

3. PDF Document (.pdf) - Professional format

   • Clean speaker attribution
   • Formatted for sharing
   • Optional timestamps

4. SRT Subtitles (.srt) - For video with speaker names in captions

   1
   00:00:01,000 --> 00:00:03,500
   [John Smith]: This is the first point.
   

How to Export

1. Open your diarized transcript
2. Click “Download” button
3. Select format (TXT, DOCX, PDF, SRT)
4. File downloads with speaker names included

Speaker name preservation:

• All formats include assigned speaker names
• Generic labels (Speaker 1, 2, 3) used if names not assigned
• Timestamps included in Word, PDF, and SRT formats

Speaker Diarization vs Manual Labeling

Understanding when automatic diarization saves time:

When to use automatic diarization:

• General business meetings
• Podcasts and interviews
• Most research applications
• Content creation
• Internal documentation

When manual review is essential:

• Legal depositions
• Medical consultations
• High-stakes business negotiations
• Published research
• Compliance-critical recordings

Hybrid approach (best practice):

1. Use automatic diarization for initial pass
2. Manually review accuracy
3. Correct any errors
4. Verify critical segments
5. Export final version

Advanced Diarization Features

AI Speaker Name Detection

For certain content, AI can suggest speaker names:

How it works:

1. AI analyzes transcript context
2. Looks for self-introductions (“Hi, I’m John…”)
3. Detects patterns (host vs guest, interviewer vs subject)
4. Suggests names based on context

When available:

• Interviews with formal introductions
• Podcasts with host/guest structure
• Meetings where participants introduce themselves

Accepting suggestions:

1. Review AI-suggested names
2. Verify they match correct speakers
3. Accept or modify as needed
4. AI learns from your corrections

Team Member Integration

Connect speakers to your workspace:

1. Assign meeting participants to team members
2. Speaker labels link to user profiles
3. Auto-tag team members in transcripts
4. Track individual contributions across meetings

Benefits:

• Consistent speaker names across all meetings
• Link to email/profile
• Analytics by team member
• Searchable by person

Multi-Language Diarization

ScreenApp diarizes in 100+ languages:

1. Upload audio in any language
2. AI detects language automatically
3. Diarization works regardless of language
4. Speaker names can be any language

Supported languages: All languages supported for transcription also support diarization

Privacy and Speaker Data

ScreenApp handles speaker data securely:

Data protection:

• Voice fingerprints generated temporarily for diarization
• Not stored after processing completes
• Speaker names controlled by you
• No third-party sharing
• Delete anytime

For sensitive recordings:

• Use anonymized speaker labels (Participant 1, 2, 3)
• Don’t assign real names if privacy required
• Control who can access transcripts
• Delete after analysis complete

Next Steps

Now that you understand speaker diarization, explore these related topics:

• How to Transcribe Audio to Text - Master transcription basics
• Meeting Notes Best Practices - Use diarization for better meeting docs
• How to Summarize Videos - Extract key points by speaker

Try Speaker Diarization Today

ScreenApp makes speaker identification effortless with automatic diarization, AI name suggestions, and easy speaker assignment. Transform multi-speaker recordings into organized, attributable transcripts.

Ready to identify speakers in your first recording? Try ScreenApp’s Speaker Diarization for free and follow this guide.