What is Speaker Diarization?

Speaker diarization is a process that separates individual speakers in an audio stream, allowing each speaker's utterances to be separated and labeled with their unique audio characteristics. This feature can also be called speaker labels or speaker change detection. It is used to increase transcript readability and better understand what a conversation is about. Common use cases for speaker diarization include audio/video management, compliance, conversational AI, education, health, law enforcement, recruiting, sales enablement, and speaker analysis. The main metric used for speaker diarization in the business world is the accuracy of identifying individual speakers or "who spoke what." Deepgram's speaker diarization has several benefits, including no need to specify the number of speakers in the audio, no cap on the number of speakers, support for any language Deepgram transcribes, and support for both pre-recorded or real-time streaming audio.