🎯 Core Speaker Features Overview

📊 Feature Specifications

🎤 Speaker Diarization:

Accuracy rate: 85% in optimal conditions
Maximum speakers: 10 speakers per recording
Language support: Works across all 104 languages
Processing speed: Real-time during live recording
Output format: Generic "Speaker 1, 2, 3" labels

🏷️ Speaker Identification:

Setup method: Manual labeling required
Voice profiles: Basic profile creation available
Name assignment: Custom speaker names supported
Cross-session memory: Limited profile persistence
Training required: 10+ minutes per speaker recommended

⚡ Real-time Capabilities

📱 Live Recording:

• Real-time speaker separation
• Instant speaker labels
• Live transcript updates
• Dynamic speaker detection

🔄 Post-processing:

• Manual speaker correction
• Name assignment editing
• Segment merging/splitting
• Timeline adjustments

💾 Export Options:

• Speaker-labeled transcripts
• Timestamped segments
• Multi-format support
• Custom naming schemes

🔍 Detailed Feature Analysis

🎭 Speaker Diarization Deep Dive

🧠 How It Works:

1. Voice Fingerprinting: Creates unique acoustic signatures for each speaker
2. Clustering Analysis: Groups similar voice patterns together
3. Change Point Detection: Identifies when speakers switch
4. Segment Assignment: Labels each audio segment with speaker ID
5. Quality Optimization: Refines boundaries for better accuracy

📊 Performance Metrics:

✅ Optimal Conditions:

• 85%+ accuracy: Clear audio, distinct voices
• 2-4 speakers: Best performance range
• Good audio quality: Minimal background noise
• Turn-taking speech: Speakers don't overlap

⚠️ Challenging Conditions:

• 65-75% accuracy: Poor audio quality
• 5+ speakers: Performance degrades
• Similar voices: Confusion between speakers
• Overlapping speech: Reduced separation quality

🏷️ Speaker Identification System

📋 Manual Setup Process:

Initial Setup:

1. Record training session
2. Review auto-generated speakers
3. Manually assign names
4. Correct misidentifications
5. Save speaker profiles

Ongoing Maintenance:

• Review each recording
• Fix speaker labeling errors
• Update profiles as needed
• Add new team members
• Monitor accuracy trends

💾 Profile Management:

Profile Creation

Basic voice characteristics stored locally per project

Cross-session Use

Limited profile persistence between recordings

Profile Updates

Manual refinement required for accuracy improvement

🌍 Language and Accent Support

🗣️ Multilingual Speaker Detection

📊 Language Coverage:

104 languages supported: Full speaker diarization capability
Major language families: Indo-European, Sino-Tibetan, Afro-Asiatic
Regional variants: Multiple dialects per language
Code-switching: Limited support for mixed languages
Accent variations: Moderate robustness across accents

🎯 Performance by Language Group:

🥇 Excellent (85%+ accuracy)

English, Spanish, French, German, Mandarin, Japanese

🥈 Good (75-85% accuracy)

Portuguese, Italian, Dutch, Korean, Arabic, Hindi

🥉 Moderate (65-75% accuracy)

Lesser-used languages, heavy accents, dialects

🌐 Mixed Language Meetings

💡 Best Practices for Multilingual Sessions:

🎯 Optimization Tips:

• Set primary meeting language correctly
• Use separate recordings per language when possible
• Ensure clear pronunciation of names
• Minimize rapid language switching
• Allow adaptation time for accent recognition

⚠️ Common Challenges:

• Code-switching mid-sentence
• Heavy accents in secondary languages
• Cultural pronunciation differences
• Mixed alphabet systems
• Varied speaking speeds by language

🎯 Accuracy Optimization Guide

📈 Pre-recording Optimization

🎤 Audio Setup:

Individual microphones: Best for distinct speaker separation
Optimal distance: 6-12 inches from each speaker
Noise reduction: Use quiet environment or noise cancellation
Audio quality: 44.1kHz sample rate minimum
Volume consistency: Balance audio levels across speakers

👥 Meeting Structure:

Speaker introductions: Clear name pronunciation at start
Turn-taking: Avoid simultaneous speaking
Speaking pace: Moderate speed for better recognition
Consistent participation: Each speaker should talk regularly
Meeting moderation: Designate someone to manage turns

⚙️ Platform Configuration

📱 Recording Settings:

Language Settings

• Select primary language
• Enable auto-detection if mixed
• Set regional variant
• Configure accent preferences

Quality Settings

• Choose highest quality mode
• Enable noise suppression
• Set optimal bit rate
• Configure speaker count

Processing Options

• Enable real-time processing
• Set speaker detection sensitivity
• Configure transcript format
• Enable timestamp precision

🔧 Post-recording Enhancement

✏️ Manual Corrections:

Speaker label review: Verify all speaker assignments
Segment merging: Combine incorrectly split segments
Speaker separation: Split merged different speakers
Timeline adjustment: Fine-tune speaker change points
Name standardization: Ensure consistent speaker naming

📊 Quality Assurance:

Accuracy spot checks: Review random 5-minute segments
Pattern identification: Note recurring errors
Improvement tracking: Monitor accuracy over time
Feedback loop: Apply learnings to future recordings
Profile updates: Refine speaker voice models

⚠️ Limitations and Workarounds

🚫 Key Limitations

🔢 Technical Limits:

10 speaker maximum: Cannot handle larger groups effectively
No automatic identification: Requires manual name assignment
Limited voice memory: Weak cross-session speaker recognition
No voice training: Cannot learn speaker preferences
Basic profile system: Simple voice characteristic storage

📉 Performance Challenges:

Similar voices: Difficulty distinguishing family members
Background noise: Reduced accuracy in noisy environments
Overlapping speech: Poor handling of interruptions
Whispered speech: Cannot detect very quiet speakers
Audio quality dependency: Requires good recording conditions

💡 Workaround Strategies

🔧 Technical Workarounds:

Large Groups (10+ people):

• Split into smaller recording sessions
• Use multiple devices for different groups
• Focus on primary speakers only
• Use meeting moderation to control turns
• Consider hybrid manual/auto approach

Similar Voices:

• Manual speaker announcement
• Use visual cues in video calls
• Assign different microphones
• Post-recording manual correction
• Create detailed speaker profiles

🔄 Process Workarounds:

Pre-meeting

• Test audio setup
• Prepare speaker list
• Brief participants
• Set speaking guidelines

During meeting

• Monitor speaker detection
• Note problem areas
• Manage speaking turns
• Ensure clear speech

Post-meeting

• Review accuracy
• Make corrections
• Update profiles
• Document issues

🏆 How Notta Compares

Platform	Speaker Accuracy	Max Speakers	Auto Identification	Voice Training	Languages
📝 Notta	85%	10	❌ Manual	⚠️ Basic	🥇 104
🔥 Fireflies	88%	Unlimited	✅ Calendar	⚠️ Basic	69
🦦 Otter.ai	83%	10	✅ Voice learning	✅ Advanced	1 (English)
🎥 Tldv	80%	20	✅ Meeting participants	⚠️ Limited	30+
📊 Rev.ai	92%	Unlimited	⚠️ API only	✅ Custom models	36

🎯 Notta's Competitive Position:

🥇 Wins:

• Most languages supported (104)
• Best multilingual accuracy
• Cost-effective pricing
• Real-time translation

⚠️ Middle Ground:

• Good overall accuracy (85%)
• Standard speaker limit (10)
• Basic profile management
• Manual identification process

❌ Gaps:

• No automatic identification
• Limited voice training
• Weak cross-session memory
• Basic integration options

💼 Use Case Recommendations

✅ Ideal Use Cases for Notta

🌍 International Teams:

Global organizations: Multiple languages in meetings
Customer support: International client interactions
Remote teams: Distributed workforce with language diversity
Educational settings: Language learning or international classes
Conference calls: Multi-national participants

💰 Budget-Conscious Users:

Small businesses: Cost-effective transcription needs
Startups: Early-stage companies with limited budgets
Freelancers: Independent professionals
Non-profits: Organizations with funding constraints
Students/researchers: Academic use cases

❌ Not Ideal Use Cases

🏢 Enterprise Requirements:

Large teams (15+ people): Exceeds speaker limit
Automated workflows: Requires manual speaker setup
High-frequency use: Speaker memory limitations
Advanced analytics: Limited speaker insights
Integration-heavy environments: Basic API capabilities

📊 High-Accuracy Needs:

Legal proceedings: Requires higher accuracy than 85%
Medical documentation: Critical accuracy requirements
Financial compliance: Strict regulatory standards
Technical support: Complex terminology challenges
Quality assurance: Precise speaker attribution needed

Quick Answer 💡