What is the difference between speaker detection and diarization?

Diarization is the audio part: which voice spoke when. Active speaker detection links that voice to a face in the picture so a crop can follow it.

Does speaker detection work with more than two people?

It can, but accuracy drops as faces get smaller and people interrupt each other. Panels and group calls need the most review.

Glossary

Speaker detection

Speaker detection is the part of video and audio analysis that works out who is speaking and when. Audio-side, it is often called speaker diarization: splitting a transcript into “speaker 1” and “speaker 2” segments. Video-side, active speaker detection matches the voice to a face on screen, usually from lip movement and face tracking. Clipping and reframing tools use it to decide where to point the vertical crop in a podcast or interview, and to label captions by speaker. It fails in predictable places: people talking over each other, off-camera voices, profile shots, masks, and wide shots where faces are small. That is why speaker-based crops need review.

In a HypeNest pass

In HypeNest, reframing aims to keep the active speaker in the vertical frame on interview and podcast clips. Watch the switches between speakers during review — a crop that lands on the listener while the guest delivers the punchline is a reason to recut or drop the clip.

Interview clip maker

FAQ

What is the difference between speaker detection and diarization?: Diarization is the audio part: which voice spoke when. Active speaker detection links that voice to a face in the picture so a crop can follow it.
Does speaker detection work with more than two people?: It can, but accuracy drops as faces get smaller and people interrupt each other. Panels and group calls need the most review.

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Related terms

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