Speaker diarization
Speaker diarization is the process of separating an audio recording into speaker-labeled segments to show who spoke when during a call or conversation.
How speaker diarization works
A diarization system analyzes changes in voice characteristics across the audio, groups segments that appear to come from the same person, and assigns labels such as Speaker A and Speaker B. It can operate alongside speech-to-text so a transcript contains both the words and the likely speaker for each segment.
Diarization does not necessarily identify a person by name. It answers “which speaker said this?” within the recording, not “who is this person?” A separate source of context may map a label to a role, such as caller or agent. When each side of a phone call is recorded on a separate audio channel, channel information can provide a stronger boundary than analyzing a mixed recording alone.
Overlapping speech is a central challenge. Two people may speak at once, interrupt each other, or produce very short acknowledgements. Background voices, similar-sounding speakers, poor audio quality, and transfers that introduce a new participant can also cause labels to merge or switch.
Why it matters for AI phone calls
Speaker labels make a transcript easier to interpret. They help a reviewer distinguish the caller's request from the agent's response, understand who supplied a detail, and follow how an escalation or transfer unfolded. Downstream summaries, call scoring, quality review, and analytics can all be misleading if statements are attributed to the wrong participant.
Accuracy should be evaluated at the segment level, especially around interruptions and handoffs. A transcript can contain the correct words while assigning them to the wrong speaker, so transcription quality alone does not verify diarization quality.
Teams should test ordinary two-party calls as well as speakerphone conversations, overlapping speech, quiet callers, transfers, conferences, and long periods of hold audio. For consequential review, the recording remains important context: speaker labels are generated metadata and should not be treated as infallible proof of identity.