Unifying Diarization, Separation, and ASR with Multi-Speaker Encoder

--This paper presents a unified multi-speaker encoder (UME), a novel architecture that jointly learns representations for speaker diarization (SD), speech separation (SS), and multi-speaker automatic speech recognition (ASR) tasks using a shared speech foundational encoder . We leverage the hidden representations from multiple layers of UME as a residual weighted-sum encoding (RWSE) to effectively use information from different semantic levels, contributing to bottom-up alignment between tasks. Our evaluations demonstrate that UME substantially improves over the single-task baselines dedicated to SD, SS, and multi-speaker ASR on LibriMix evaluation sets. Notably, for SD, UME outperforms the previous studies, achieving diarization error rates of 1.37% and 2.29% on Libri2Mix and Libri3Mix evaluation sets, respectively. Speaker diarization (SD), speech separation (SS), and multi-speaker automatic speech recognition (ASR) are tasks of great importance that aim to comprehend and answer the question "who spoke what and when," with applications to transcribing meetings and interviews, among others.