Speaker Diarization — Identifying Who Said What in Audio Recordings
Definition
Speaker diarization is the automatic process of partitioning an audio recording into segments labeled by speaker identity. It answers the question "who spoke when?" without requiring prior knowledge of the speakers' voices. In meeting intelligence, diarization transforms a monolithic audio stream into a structured, speaker-attributed transcript that enables per-speaker analysis.
Why it matters
Without diarization, a transcript is just a wall of text. With it, every statement is tied to a specific person, enabling downstream analysis: who dominated the conversation, who raised the key objection, which speaker's engagement changed over time. Accurate speaker attribution is the foundation upon which all higher-level meeting intelligence is built — summaries, behavioral analysis, and speaker-specific insights all depend on getting the "who" right.
Common mistakes
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Tools that use speaker diarization
Most meeting intelligence tools include some form of speaker diarization, from basic voice clustering (Otter.ai, Fireflies.ai) to enterprise-grade attribution (Gong). auraScribe takes a distinctive approach with its 2-pass architecture — acoustic diarization in Pass 1 followed by text-based identification in Pass 1.5 — and adds a human-in-the-loop review stage where users correct attributions before behavioral analysis begins.