Speaker Detection & Diarization
Automatically identify and label different speakers in your audio and video transcriptions. Know exactly who said what.
What is Speaker Diarization?
Speaker diarization is the process of partitioning an audio stream into segments according to the identity of the speaker. In simpler terms, it answers the question "who spoke when?" 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.
How Speaker Detection Works
1. Voice Activity Detection
The system first identifies which segments of audio contain speech versus silence, music, or background noise.
2. Speaker Embedding
Each speech segment is converted into a speaker embedding -- a compact vector that captures the unique vocal characteristics of the speaker.
3. Clustering & Labeling
Embeddings are clustered to group segments from the same speaker together, then each cluster is assigned a label (Speaker 1, Speaker 2, etc.).
Use Cases for Speaker Detection
Speaker Detection on 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.
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