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Joint modeling of multi-speaker ASR and speaker diarization has recentlyshown promising results in speaker-attributed automatic speech recognition(SA-ASR).Although being able to obtain state-of-the-art (SOTA) performance,most of the studies are based on an autoregressive (AR) decoder which generatestokens one-by-one and results in a large real-time factor (RTF). To speed upinference, we introduce a recently proposed non-autoregressive model Paraformeras an acoustic model in the SA-ASR model.Paraformer uses a single-step decoderto enable parallel generation, obtaining comparable performance to the SOTA ARtransformer models. Besides, we propose a speaker-filling strategy to reducespeaker identification errors and adopt an inter-CTC strategy to enhance theencoder's ability in acoustic modeling. Experiments on the AliMeeting corpusshow that our model outperforms the cascaded SA-ASR model by a 6.1% relativespeaker-dependent character error rate (SD-CER) reduction on the test set.Moreover, our model achieves a comparable SD-CER of 34.8% with only 1/10 RTFcompared with the SOTA joint AR SA-ASR model.

SA-Paraformer: Non-autoregressive End-to-End Speaker-Attributed ASR