Speaker diarization vs speaker recognition - what's the difference?

The field of audio analysis is increasingly important as applications incorporate speech data. Two commonly used terms are "speaker diarization" and "speaker recognition." Speaker diarization partitions an audio file into segments according to speaker identity, without prior knowledge of the speakers' personal identities. It is useful in scenarios where understanding the structure of a conversation is important, such as business meetings or call center recordings. Speaker recognition involves analyzing vocal patterns to determine or verify the identity of a speaker and can be used for security systems, voice-activated devices, and other applications requiring speaker identification. Combining these techniques can create a robust audio analysis pipeline, leveraging the strengths of each method to achieve more accurate and comprehensive results.