Speakererkennung & Diarisierung

Was ist Speaker Diarization?

Speaker-Diarisierung ist der Prozess der Partitionierung eines Audio-Streams in Segmente nach der Identität des Sprechers. Einfacher gesagt, beantwortet es die Frage "Wer sprach wann?" 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.

Wie Lautsprechererkennung funktioniert

Anwendungsfälle für Lautsprechererkennung

Speaker-Erkennung auf 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.

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.