Speakerdetectie en -diarisatie

Automatisch identificeren en labelen van verschillende luidsprekers in uw audio-en video transcripties. Weet precies wie zei wat.

Werkt met publiek beschikbare audio & video. DRM-beschermde inhoud wordt niet ondersteund.

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Wat is Speaker Diarization?

Speaker diarization is het proces van het partitioneren van een audiostream in segmenten volgens de identiteit van de spreker. In eenvoudiger termen, het beantwoordt de vraag "wie sprak wanneer?" This is essential for multi-speaker recordings like meetings, interviews, podcasts, conference calls, and legal proceedings where knowing who said what is just as important as what was said.

STT.ai uses advanced neural speaker diarization models that can detect and label speakers in real time. The system creates speaker embeddings -- numerical representations of each voice's unique characteristics -- and clusters them to distinguish between different people. This works even when speakers have similar voices or frequently interrupt each other.

Hoe Speaker Detectie werkt

1. Voice Activity Detection

Het systeem identificeert eerst welke segmenten van audio spraak versus stilte, muziek of achtergrondruis bevatten.

2. Inbedding van de voorzitter

Elk spraaksegment wordt omgezet in een speaker-inbedding -- een compacte vector die de unieke vocale kenmerken van de speaker vastlegt.

3. Clustering en etikettering

Inbeddingen worden geclusterd om segmenten van dezelfde luidspreker samen te groeperen, dan krijgt elk cluster een label toegewezen (luidspreker 1, luidspreker 2, enz.).

Gebruik koffers voor Speaker Detectie

Meeting Transcription
Noteer automatisch elke deelnemer aan meeting opnames. Genereer minuten met duidelijke toeschrijving van wie wat zei.
Podcast-transcriptie
Onderscheid tussen host en gasten in podcast-episodes. Maak shownotities aan met de juiste speakertoeschrijving.
Interview Transcription
Aparte interviewer en interviewee reacties voor onderzoek, journalistiek, en het inhuren van documentatie.
Juridische & naleving
Maak officiële verslagen van verklaringen, hoorzittingen, en naleving oproepen met duidelijke luidspreker identificatie.

Speakerdetectie op STT.ai

Speaker detection is available on all paid plans. When you transcribe audio or video with speaker detection enabled, the transcript will include speaker labels inline with the text. You can also export speaker-labeled transcripts in all supported formats including SRT, VTT, DOCX, JSON, and PDF.

Speaker 1 [00:00:01]: Welcome to the meeting, everyone. Let's start with the quarterly review. Speaker 2 [00:00:05]: Thanks. I have the numbers ready. Revenue is up 23% quarter over quarter. Speaker 1 [00:00:12]: That's great news. Can you walk us through the breakdown?

The system can detect up to 20 distinct speakers in a single recording. For best results, ensure each speaker has at least a few seconds of solo speech. Overlapping speech is handled but may reduce accuracy in heavily cross-talked segments.

Probeer luidsprekerdetectie nu

Upload een multi-luidspreker opname en zie luidsprekers automatisch gelabeld.

Gratis beginnen met afschrijven

Veelgestelde vragen

speaker detection runs in your browser: paste a URL, upload a file, or record from your mic. STT.ai picks the AI model and returns the transcript in under 5 minutes. Export as TXT, SRT, VTT, DOCX, JSON, or PDF.

Yes — every visitor gets 600 free minutes/month on STT.ai, usable for speaker detection the same as any other workflow. Paid plans starting at $5/month unlock longer files, private transcripts, and priority queueing.

speaker detection runs on the same AI models as the rest of STT.ai — our best models reach 95-97% accuracy on clean speech (3-5% Word Error Rate on benchmarks). Switch models on the fly if the first pass is below your target.

speaker detection can run on any of STT.ai's 10+ models — STT.ai Enhanced (most accurate), Whisper Large V3 (99 languages), NVIDIA Canary (#1 WER on supported langs), Whisper Turbo (fast), Moonshine (lightweight), and more.

Yes. Every transcript exports as SRT or VTT — works with YouTube, Vimeo, TikTok, VLC, and every major video player. The burn-subtitles tool overlays them onto video as hardsubs.

Yes. Speaker diarization automatically labels each voice (Speaker 1, Speaker 2, ...) and you can rename them in the built-in editor. Works across all models and languages.

Most speaker detection jobs finish in under 5 minutes. A 1-hour audio file typically completes in 2-3 minutes with our fastest models. Speed depends on chosen model and current GPU load.

speaker detection accepts 20+ formats — MP3, WAV, M4A, FLAC, OGG, MP4, MKV, MOV, WebM, AVI, and more. Output to TXT, SRT, VTT, DOCX, JSON, or PDF.

Yes. Audio files submitted to speaker detection are processed and deleted by default. Pro plans add client-side encryption — even if STT.ai's database is breached, your transcripts are unreadable without your key. Data is never used for model training without explicit opt-in.

Yes. STT.ai offers a REST API with Python and Node.js SDKs, plus an MCP server for Claude and Cursor — all usable for speaker detection workflows. Free API tier includes 100 minutes/month.

Yes. Every transcript opens in the built-in editor where you can correct words, rename speakers, adjust timestamps, and add notes. All changes save automatically.

Every transcript gets a unique shareable URL. Export to DOCX or PDF for email. Pro plans add password-protected and permanent links — useful for client work.

STT.ai handles 1,300+ platforms including YouTube, Vimeo, TikTok, SoundCloud, Zoom, Google Meet, podcast hosts, and more. URL transcription works with publicly-available content only — DRM-protected sources can't be transcribed.