Zhennan Lin

Heavy speaker overlap drastically hinders speech recognition accuracy in smart glasses, revealing critical limitations in current audio-language models.

Multi-speaker conversational understanding is critically under-evaluated, with MSU-Bench revealing that even leading models struggle with complex speaker grounding tasks.

Achieving high accuracy in multi-speaker transcription, SoulX-Transcriber outperforms existing models by effectively addressing speaker overlap and rapid turn-taking.