Speaker Diarization - Identify Speakers Automatically
This tool automatically identifies who is speaking and when in multi-person audio recordings. Upload a podcast, meeting, or interview and get timestamped speaker labels in 3-5 minutes - no manual work required.
ChatGPT cannot identify individual speakers in audio recordings. This tool processes multi-speaker files and labels who spoke when - a capability AI chatbots don’t have for uploaded audio.
Why use this tool:
- 96-98% accuracy in optimal conditions (clear audio, minimal background noise)
- Handles 2-10 speakers per file (optimal: 2-5 speakers)
- Processes 1-hour audio in approximately 4 minutes
- Works with MP3, WAV, M4A, FLAC formats (up to 500MB)
- Free tier: 3 audio files monthly (up to 45 minutes each)
- Speaker labels include timestamps down to the second
Perfect for podcasters who need speaker-separated transcripts, business teams tracking meeting participation, or researchers attributing quotes to specific participants.
How Speaker Identification Works
Using this tool takes three simple steps:
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Upload your audio file - The tool accepts MP3, WAV, M4A, and FLAC files up to 500MB. Drag and drop or paste a URL from podcast hosting platforms. Best results: mono or stereo recordings with distinct speakers.
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AI analyzes voice patterns - The system identifies unique voice characteristics (pitch, tone, speaking rate) for each speaker. Processing takes 3-5 minutes for most files. The AI handles overlapping speech and speaker interruptions automatically.
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Download speaker-labeled transcript - Each speaker receives a unique identifier (Speaker 1, Speaker 2, etc.). Export includes timestamps showing exactly when each person spoke. Choose TXT, DOC, PDF, or SRT formats.
The AI achieves 96-98% accuracy with 2-5 speakers in clear audio. Accuracy decreases slightly with 6-10 speakers or when background noise is present. Works across multiple languages with accent-adaptive analysis.
Speaker Diarization vs Other Tools
| Feature | ScreenApp | AudioPod | Happy Scribe | Descript | Sonix |
|---|---|---|---|---|---|
| Free tier | 3 files (45 min) | No free tier | 10 min trial | 1 hour free | 30 min trial |
| Max speakers | 10 | 8 | 10 | Unlimited | 10 |
| Accuracy | 96-98% | 94-96% | 95-97% | 96-99% | 95-98% |
| Overlapping speech | Yes | Limited | Yes | Yes | Yes |
| File upload | Yes | Yes | Yes | Yes | Yes |
| Real-time processing | No | Yes | No | No | No |
| Export formats | TXT, DOC, PDF, SRT | TXT only | TXT, PDF, SRT | Multiple | Multiple |
| Languages | 100+ | 40+ | 120+ | 50+ | 100+ |
| Paid pricing | $19/mo | $29/mo | $17/mo | $12/mo | $22/mo |
Key differences:
- vs AudioPod: AudioPod offers real-time speaker separation but has no free tier and costs $29/month from day one. ScreenApp provides 3 free audio files monthly (45 minutes each) before requiring payment, and handles 10 speakers vs AudioPod’s 8-speaker limit.
- vs Happy Scribe: Happy Scribe’s free trial is limited to 10 minutes of audio. ScreenApp offers 45 minutes per file with 3 files monthly. Both achieve similar accuracy (96-98% vs 95-97%), but ScreenApp’s free tier is more generous.
- vs Descript: Descript handles unlimited speakers with 96-99% accuracy but charges $12/month after the 1-hour trial. ScreenApp provides ongoing free tier access (3 files monthly) for users with occasional needs.
- vs Sonix: Sonix limits the free trial to 30 minutes. ScreenApp provides 135 minutes monthly (3 x 45 min) for free. Sonix costs $22/month vs ScreenApp’s $19/month, though both support 100+ languages.
Who Needs Speaker Diarization
Podcasters
Multi-host podcasts need speaker-separated transcripts for show notes and SEO. The tool identifies each host and guest automatically, creating searchable episode archives with accurate speaker attribution. No more manually labeling who said what.
Business Teams
Meeting facilitators need speaker-identified notes to track participation and attribute action items. The system shows who contributed which ideas and decisions. Useful for remote teams where video isn’t always available.
Researchers
Academic and market researchers conducting focus groups need speaker attribution for analysis. The tool assigns consistent speaker IDs across the recording, making it easy to analyze individual responses without manual coding.
Legal and Healthcare
Law firms processing depositions and medical professionals documenting consultations require precise speaker identification for compliance. The system provides legally-admissible timestamped transcripts with speaker labels.
FAQ
What is speaker diarization?
Speaker diarization is the process of automatically identifying “who spoke when” in an audio recording. It analyzes voice characteristics (pitch, tone, speaking rate) to determine unique speakers and timestamps their speech segments. The output shows Speaker 1, Speaker 2, etc. with exact times they spoke.
How accurate is speaker diarization?
Accuracy reaches 96-98% with 2-5 speakers in clear audio conditions. Performance depends on audio quality, number of speakers, and speech overlap. With 6-10 speakers or moderate background noise, accuracy decreases to 90-94%. Poor audio quality (phone calls, outdoor recordings) typically achieves 85-90% accuracy.
Can this work with podcasts?
Yes, it works perfectly for podcasts with multiple hosts or guests. Upload your MP3 or M4A file and receive speaker-separated transcripts with timestamps. Each host and guest gets a unique identifier, making it easy to create show notes or search for specific speaker contributions.
How many speakers can it identify?
The tool reliably identifies up to 10 speakers in a single audio file. Optimal performance occurs with 2-5 speakers where accuracy stays at 96-98%. With 6-7 speakers, accuracy is 92-95%. With 8-10 speakers, expect 90-93% accuracy as voice characteristic overlap increases.
Does it work in real-time?
No, this is a processing tool, not real-time transcription. Upload a completed audio file and results arrive within 3-5 minutes depending on file length. Most 1-hour recordings process in approximately 4 minutes. For live meetings, consider the meeting recorder instead.
What audio formats are supported?
The tool accepts MP3, WAV, M4A, and FLAC files up to 500MB. For best results, use mono or stereo recordings. Multi-track recordings (each speaker on separate track) should be mixed down to stereo before upload.
How does it handle overlapping speech?
The AI detects overlapping speech and labels segments with multiple active speakers. In the transcript, overlapping sections show both speaker IDs with timestamps. This helps you identify cross-talk and interruptions that might need clarification.
Can it identify specific people by name?
No. The system assigns generic identifiers (Speaker 1, Speaker 2, etc.) based on voice characteristics. It doesn’t perform voice recognition to match specific individuals. You manually label speakers after processing (e.g., change “Speaker 1” to “John Smith” in your editor).
What languages does it support?
The tool supports 100+ languages including English, Spanish, French, German, Portuguese, Chinese, Japanese, Hindi, and Arabic. Language detection is automatic - the AI recognizes the language and adapts speaker identification accordingly. Accent-adaptive analysis works across dialects.
Is there a free tier?
Yes. The free tier includes 3 audio files (up to 45 minutes each) monthly with no credit card required. Free users get full speaker diarization features: timestamped labels, export options, and support for up to 10 speakers. The Growth plan at $19/month (annual) offers unlimited processing.
