  Joint modeling of multi-speaker ASR and speaker diarization has recently
shown 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 generates
tokens one-by-one and results in a large real-time factor (RTF). To speed up
inference, we introduce a recently proposed non-autoregressive model Paraformer
as an acoustic model in the SA-ASR model.Paraformer uses a single-step decoder
to enable parallel generation, obtaining comparable performance to the SOTA AR
transformer models. Besides, we propose a speaker-filling strategy to reduce
speaker identification errors and adopt an inter-CTC strategy to enhance the
encoder's ability in acoustic modeling. Experiments on the AliMeeting corpus
show that our model outperforms the cascaded SA-ASR model by a 6.1% relative
speaker-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 RTF
compared with the SOTA joint AR SA-ASR model.
