  Recently, speaker-attributed automatic speech recognition (SA-ASR) has
attracted a wide attention, which aims at answering the question ``who spoke
what''. Different from modular systems, end-to-end (E2E) SA-ASR minimizes the
speaker-dependent recognition errors directly and shows a promising
applicability. In this paper, we propose a context-aware SA-ASR (CASA-ASR)
model by enhancing the contextual modeling ability of E2E SA-ASR. Specifically,
in CASA-ASR, a contextual text encoder is involved to aggregate the semantic
information of the whole utterance, and a context-dependent scorer is employed
to model the speaker discriminability by contrasting with speakers in the
context. In addition, a two-pass decoding strategy is further proposed to fully
leverage the contextual modeling ability resulting in a better recognition
performance. Experimental results on AliMeeting corpus show that the proposed
CASA-ASR model outperforms the original E2E SA-ASR system with a relative
improvement of 11.76% in terms of speaker-dependent character error rate.
