Who is speaking when? Upload a conversation and AI labels speakers, tracks talk time, and maps the flow.
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Speaker Diarization is an AI-powered tool that labels different speakers throughout a conversation, identifying unique speakers, tracking speaking time, and noting transitions to create a conversational map. It distinguishes between different speakers in multi-speaker audio, identifying when each person is talking, tracking speaking time distribution, and noting distinguishing voice characteristics for each speaker. The tool creates a detailed map showing who speaks when and for how long, making it valuable for meeting transcription, interview analysis, podcast production, conversation analysis, or anyone needing to identify and track multiple speakers in audio content.
Upload your multi-speaker audio and the AI analyzes speaker characteristics systematically. It identifies unique speakers by analyzing voice characteristics including pitch, timbre, and speech patterns. Speaker transition detection identifies when speakers change. Speaking time tracking measures how much each speaker talks. Voice characteristic analysis notes distinguishing features for each speaker. The tool creates a conversational map showing speaker labels (Speaker 1, Speaker 2, etc.), timestamps for when each speaker talks, speaking time distribution, and speaker transitions. It provides detailed analysis of the conversation structure, showing who participates, when they speak, and for how long. You can provide known information about speakers in the notes field to help refine identification, such as names or roles.
Upload the conversation and the AI separates the voices, labels each one (Speaker 1, Speaker 2, and so on), and maps when each person talks: turn by turn, with transitions and each speaker's share of the total talking time. You get a conversational map instead of an undifferentiated wall of audio.
It's the technical name for the who-spoke-when problem: segmenting a multi-voice recording by speaker. Transcription captures what was said; diarization adds who said it and when. The two together are what make meeting notes, interview write-ups, and call reviews actually attributable to the right people.
By voice characteristics: pitch, timbre, pacing, and speaking style differ enough between most people to separate them. The output notes the distinguishing features it used for each speaker (one voice deeper, one faster, one with an accent), which doubles as your way to verify it mapped the right person to the right label.
Yes, the talk-time distribution is a core output. You see roughly what share of the conversation each speaker held and how the turns flowed, which is the interesting layer for meetings (who actually runs them), interviews (are you talking more than your guest), and sales calls (the listen-to-talk ratio).
Give it context in the notes: who's in the recording and anything identifying (Maria runs the meeting, the guest is the one with the accent). The mapping gets applied where the audio supports it. Without notes you get neutral labels plus voice descriptions you can match up yourself.
Two or three clearly distinct voices on a clean recording work well. Accuracy drops with crosstalk (people talking over each other is the classic failure), very similar voices, phone-quality audio, and large groups. Expect occasional label swaps in heated overlapping exchanges, and treat the map as a strong draft to spot-check rather than ground truth.
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