Speaker Diarization

Speaker diarization is the task of figuring out "who spoke when" in an audio recording. While transcription tells you what was said, diarization tells you which speaker said it. The two work hand in hand: a diarized transcript labels each segment with a speaker identity, turning a wall of text into a structured conversation.

How it works

A typical diarization pipeline involves several stages. First, the system detects which parts of the audio contain speech and which are silence or background noise, a step called voice activity detection. Next, it segments the speech into chunks and extracts speaker embeddings, which are compact numerical representations of each person's vocal characteristics. Finally, a clustering algorithm groups segments that sound like they came from the same person.

Modern systems increasingly perform diarization end-to-end using neural networks, which can handle overlapping speech and rapid turn-taking more gracefully than older pipeline approaches.

Why it matters for African language transcription

Many real-world African language recordings involve multi-speaker scenarios: radio panel discussions in Swahili, community meetings in Twi, or interview journalism in Yoruba. Without diarization, the transcript is a flat stream of words with no indication of conversational structure. With it, users can quickly identify who made a specific statement, which is essential for journalism, legal proceedings, and research.

Diarization becomes especially interesting when speakers switch between languages mid-conversation, a phenomenon closely related to code-switching. AuTrans combines diarization with language-aware transcription so that each speaker's contributions are both correctly attributed and accurately transcribed, regardless of the language they happen to be using at any given moment in the recording.