In this paper, we conduct a comparative study on speaker-attributed automatic
speech recognition (SA-ASR) in the multi-party meeting scenario, a topic with
increasing attention in meeting rich transcription. Specifically, three
approaches are evaluated in this study. The first approach, FD-SOT, consists of
a frame-level diarization model to identify speakers and a multi-talker ASR to
recognize utterances. The speaker-attributed transcriptions are obtained by
aligning the diarization results and recognized hypotheses. However, such an
alignment strategy may suffer from erroneous timestamps due to the modular
independence, severely hindering the model performance. Therefore, we propose
the second approach, WD-SOT, to address alignment errors by introducing a
word-level diarization model, which can get rid of such timestamp alignment
dependency. To further mitigate the alignment issues, we propose the third
approach, TS-ASR, which trains a target-speaker separation module and an ASR
module jointly. By comparing various strategies for each SA-ASR approach,
experimental results on a real meeting scenario corpus, AliMeeting, reveal that
the WD-SOT approach achieves 10.7% relative reduction on averaged
speaker-dependent character error rate (SD-CER), compared with the FD-SOT
approach. In addition, the TS-ASR approach also outperforms the FD-SOT approach
and brings 16.5% relative average SD-CER reduction.

对在多方会议场景下的口语识别（SA-ASR）中的发言人进行的比较研究中，通过三种不同的方法进行了评估。结果表明，引入单词级别的划分模型可以有效降低时戳对准错误。同时，采用目标发言人分离模块和 ASR 模块联合训练可以显著提高 SA-ASR 的性能。