Speaker diarization is the process of answering “who spoke when” by separating an audio stream into segments and consistently labeling each segment by speaker identity (e.g., Speaker A, Speaker B), thereby making transcripts clearer, searchable, and useful for analytics across domains like call centers, legal, healthcare, media, and conversational AI. As of 2025, modern systems rely on deep neural networks to learn robust speaker embeddings that generalize across environments, and many no longer require prior knowledge of the number of speakers—enabling practical real-time scenarios such as debates, podcasts, and multi-speaker meetings. How Speaker Diarization Works Modern diarization pipelines comprise several…
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