Label different speakers throughout a conversation. Identifies unique speakers, tracks speaking time, and notes transitions. Creates a conversational map.
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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.
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