Human-in-the-Loop Speaker Review
Speaker identification is the one area where AI frequently stumbles. Different people can sound similar. The same person's name might be spelled multiple ways. Context clues in conversation are ambiguous. auraScribe addresses this by making speaker verification a human task.
How it works
- AI proposes: After transcription and speaker profiling, auraScribe presents its best guesses for each speaker's name, role, company, and assigned transcript lines.
- Uncertainty flags: Speakers and transcript lines where the AI is uncertain are highlighted with visual indicators, directing attention to the most likely errors.
- Human verifies: Users can edit names, swap speakers (when two speakers' lines are mixed up), merge duplicate speakers, or create new speakers.
- Analysis proceeds: Only after review does the final behavioral analysis (Pass 3) run, using the corrected speaker attributions.
Why this matters
A behavioral analysis is only as useful as its speaker attribution. "The sales lead showed confidence during the close" is valuable coaching. "The client showed confidence during the close" — if the speakers are swapped — is misleading.
By pausing for human review, auraScribe ensures that every per-speaker remark, every coaching point, and every behavioral observation is attributed to the correct person.
Design philosophy
It is always better to create an extra speaker that can be merged than to miss one. A missing speaker is irrecoverable; a duplicate can be merged by the user in seconds. This principle is embedded in every prompt and every piece of speaker-handling logic.