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Speaker identification accuracy skyrockets with SpeakerRPL v2, slashing error rates by 93% through enhanced few-shot tuning and model fusion.
LLMs can perfectly cluster speakers in overlapping multi-party conversations, enabling near-perfect Joint ASR-Clustering Error Rate in challenging CHiME-9 tasks.