Speaker Diarisation: Identify Who Said What
Automatically detect and label individual voices in multi-speaker recordings. View speaker-attributed transcripts and talk-time analytics.
Automatic Speaker Identification in Any Recording
Speakers uses diarisation to detect distinct voices in your recordings and label each segment with the correct speaker. The result is a transcript where every sentence is attributed, making conversations, interviews, and meetings easy to follow and search.
- Detects and separates individual speakers without prior voice samples
- Labels every transcript segment with the identified speaker
- Shows talk-time distribution and speaking pattern breakdowns
- Supports custom speaker naming for recurring participants
- Works with uploaded recordings and live-captured audio
How It Works
Upload or Record
Process any multi-speaker recording. Speaker detection runs automatically during transcription.
Review Attribution
See speaker labels inline throughout the transcript. Rename detected speakers for clarity.
Analyse Participation
View talk-time charts and turn-taking patterns to understand who contributed what.
Transcripts Without Attribution Are Just Walls of Text
When multiple people speak in a recording, an unlabelled transcript is nearly unusable. Speaker diarisation transforms it into an accountable, searchable record where every statement is tied to a person.
Who It's For
Meeting Organisers
Attribute decisions and action items to specific team members in the transcript.
Qualitative Researchers
Track individual participant responses across focus group and interview recordings.
Podcast Producers
Generate labelled transcripts separating host and guest dialogue for show notes.
HR and Legal Teams
Produce speaker-separated interview and deposition records for compliance and review.
Part of the TranscriptAI Ecosystem
Speaker labels enrich Meeting Mode by attributing actions and decisions to individuals. They also appear in transcript search results and exported documents.