Streaming speaker diarization: How to identify who's speaking in real time

Streaming speaker diarization is a technology that identifies who is speaking in real-time during live audio sessions by assigning speaker labels like SPEAKER_A and SPEAKER_B as conversations happen. Unlike traditional batch diarization, which processes complete recordings and allows for revisions, streaming diarization makes immediate and irreversible speaker assignments, trading some accuracy for speed. This capability is crucial for applications needing real-time speaker identification, such as voice agents, live contact center coaching, and meeting platforms that display labeled transcripts. It works by processing audio as it's received, using speech-to-text technology to detect when a speaker finishes talking and creating a voice fingerprint to determine if the speaker is recognized or new. Streaming diarization faces challenges with overlapping speech, short utterances, and background noise, but improvements in speaker embedding models have enhanced its reliability. This approach is ideal for scenarios where immediate speaker attribution is necessary, while batch processing remains suitable for post-conversation analysis requiring higher accuracy.

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