← Brahmaa One
Speaker Diarisation

Know exactly
who said what.

AI separates every speaker in your meeting and transcribes what each person said — with names, timestamps, talk-time analytics, and speaker filtering.

🗣️ Speaker breakdownSales demo · 48 min · 3 speakers
3
Speakers
98%
Accuracy
48min
Duration
🧑
Arjun (Sales rep) — 58% talk time
28 min · 142 sentences · 12 questions asked
28 min
🧑
Priya (Client) — 34% talk time
16 min · 68 sentences · 4 objections raised
16 min
🧑
Rohan (Client IT) — 8% talk time
4 min · 22 sentences · 3 technical questions
4 min
98%+
Speaker separation accuracy
20+
Speakers handled per recording
100%
Statements timestamped per speaker
Auto
Speaker recognition across recordings
Features

Every speaker.
Perfectly identified.

🗣️
Automatic speaker detection
AI detects and separates every speaker in the meeting — even in calls with 10+ participants — without any manual configuration.
🏷️
Assign real names
Replace "Speaker 1, Speaker 2" with real names in one click. Names are remembered for future recordings with the same participants.
📊
Talk-time analytics
See exactly how long each person spoke, their share of airtime, and how many times they were interrupted. Data per speaker, per meeting.
🎯
98%+ diarisation accuracy
Speaker separation is accurate even with similar voice profiles, overlapping speech, and variable audio quality across participants.
👥
20+ speaker support
Handles panels, all-hands, group interviews, and team standups with 2 to 20+ participants without losing attribution accuracy.
⏱️
Timestamped per speaker
Every sentence is timestamped per speaker so you can jump to exactly when a specific person said something in the original recording.
🔍
Filter by speaker
Filter the entire transcript to show only one person's contributions. Review what the client said, what the manager committed to, or what the candidate answered.
📤
Attributed exports
Every export format includes full speaker attribution with names and timestamps. Paste into any document and the context comes with it.
🔗
Cross-recording speaker profiles
Once a speaker is named, Brahmaa One recognises them in future recordings automatically. Your team's voices are remembered across your entire library.
How it works

Every voice separated,
every word attributed.

Speaker diarisation runs alongside transcription — analysing voice characteristics in real time to attribute each sentence to its speaker before the meeting ends.

  • Audio is analysed for distinct voice profiles as the meeting runs
  • Each speaker is given a label (Speaker 1, 2, 3…) in real time
  • After the call, rename speakers with real names in one click
  • Talk-time analytics generated automatically — no manual calculation
  • Filter transcript view to any single speaker with one click
  • Speaker profiles linked across future recordings automatically
🔍 Filter: Priya (Client) only68 sentences · 16 minutes
🧑
Priya · 08:14
"The budget is a concern — we're working with ₹3L this quarter."
08:14
🧑
Priya · 22:41
"If onboarding takes more than 2 weeks we'll need to revisit timeline."
22:41
🧑
Priya · 38:05
"Send me the proposal by Friday and I'll loop in the CTO."
38:05
Use cases

When attribution
matters most.

Speaker diarisation changes how teams use recordings — from passive archives to active intelligence tools.

📞
Sales call analysis

Separate the rep's pitch from the client's responses. Analyse what the client actually said — not the mixed conversation. Coach reps on talk-time ratio and objection handling.

⚖️
Legal documentation

Every statement attributed to the specific person who made it. Disputes about who said what are resolved by searching the speaker-filtered transcript instantly.

🎓
Interview transcription

Candidate answers separated from interviewer questions automatically. Hiring teams review only the candidate's responses without filtering through the full transcript.

🏢
Multi-stakeholder meetings

Board meetings, client reviews, and partner calls with 6+ participants all produce clearly attributed transcripts — no confusion about who proposed what.

🔬
User research

Focus groups and panel interviews produce a transcript where every participant's input is individually attributed. Researchers analyse responses by participant without manual separation.

📺
Podcast and media

Multi-guest podcasts and panel discussions transcribed with each speaker clearly labelled. Producers get show notes with accurate speaker attribution automatically.

FAQ

Questions about Speaker Diarisation.

Speaker Diarisation

Every voice.
Every word.

Accurate speaker-separated transcripts for every meeting, automatically.