What's the best way to label speakers in a transcript? I measured every option (lyonesse.app)

A recent study evaluated various speaker-diarization pipelines to determine the most effective method for labeling speakers in transcripts, particularly for live streaming scenarios. The analysis involved scoring systems against real meeting data and revealed that using a domain-specific trained checkpoint significantly improved performance over the default model—specifically, switching from the DIHARD 3 checkpoint to the AMI checkpoint reduced the Diarization Error Rate (DER) from 38.46% to 10.87%. This emphasizes the importance of model selection based on the data context, as some configurations yield vastly different results even with the same architecture. Moreover, the study highlighted critical implications regarding parameter tuning, cautioning against optimizing settings based on synthetic audio, which can mislead developers into making erroneous assumptions about performance on real-world audio. It was found that tuning parameters based on overly simplistic datasets could result in misestimating the effectiveness of configurations in diverse environments. The findings ultimately underscore the necessity for AI/ML practitioners to prioritize in-domain evaluations and report both speaker counts and error rates, as errors in speaker identification can significantly impact user experience in automated transcription services.