Speaker Diarization — Accurate Speaker Identification With Human Review
What it is
Speaker diarization is the process of identifying and labeling individual speakers in an audio recording. auraScribe's diarization goes beyond basic "Speaker 1, Speaker 2" labels — it identifies speakers by name, assigns job titles and company affiliations, extracts signature voice samples, and flags uncertain attributions for human review.
Why it matters
A transcript without accurate speaker labels is a wall of text. Knowing who said what transforms it into actionable meeting intelligence. When your analysis says "the prospect hesitated on pricing," you need to know which speaker is the prospect. When behavioral patterns show someone dominating the conversation, the name matters. Accurate diarization is the foundation that makes every downstream insight meaningful.
How auraScribe does it
auraScribe uses a 2-pass approach for speaker identification in its default pipeline. Pass 1 performs acoustic-only diarization — grouping speech segments by voice similarity without attempting to assign names. Pass 1.5 then analyzes the transcript text to deduce speaker names, roles, and companies from conversational context. This separation eliminates the common problem of voice IDs getting shuffled when the model tries to do everything at once. After identification, you review the results: merge duplicate speakers, correct names, reassign misattributed lines, and resolve AI-flagged uncertainties. Only then does the behavioral analysis begin, using your corrected speaker data.
Who it's for
- Anyone recording multi-party meetings who needs to know who said what
- Sales teams tracking prospect and champion participation across calls
- Researchers conducting group interviews who need per-participant analysis
- Legal professionals requiring accurate speaker attribution in depositions
Frequently Asked Questions
How many speakers can it handle?
auraScribe reliably handles meetings with up to 10-12 speakers. Larger groups work but accuracy decreases with more participants, especially if speakers have similar voices or speak infrequently. The human review step lets you catch and correct any misattributions regardless of group size.
What if the AI gets a speaker wrong?
That is exactly what the review stage is for. auraScribe flags uncertain speaker attributions with a visual indicator. You can reassign individual lines, swap two speakers' entire transcript entries, merge duplicates, or create new speakers. All corrections are applied before the behavioral analysis runs, so your final report reflects accurate attributions.
Does it remember speakers across meetings?
Yes. auraScribe maintains a personal speaker database that learns names, roles, and communication styles over time. When you correct a speaker in one meeting, that information carries forward to future meetings where the same person appears.
How does it identify speakers by name?
The AI deduces names from conversational context — introductions, how people address each other, email signatures mentioned, and contextual clues. It does not use voice biometrics or require pre-enrollment. If a name cannot be determined, the speaker is labeled by voice characteristics and flagged for your review.