Speaker diarization(SD) is a classic task in speech processing and is crucial
in multi-party scenarios such as meetings and conversations. Current mainstream
speaker diarization approaches consider acoustic information only, which result
in performance degradation when encountering adverse acoustic conditions. In
this paper, we propose methods to extract speaker-related information from
semantic content in multi-party meetings, which, as we will show, can further
benefit speaker diarization. We introduce two sub-tasks, Dialogue Detection and
Speaker-Turn Detection, in which we effectively extract speaker information
from conversational semantics. We also propose a simple yet effective algorithm
to jointly model acoustic and semantic information and obtain
speaker-identified texts. Experiments on both AISHELL-4 and AliMeeting datasets
show that our method achieves consistent improvements over acoustic-only
speaker diarization systems.

本文介绍了一种从多方会议的语义内容中提取与讲话者相关信息进而改进说话人分离方法的方法，提出了两个子任务（对话检测和发言者切换检测）来有效地从对话语义中提取讲话者信息，并提出了一种简单而有效的算法来联合建模声学和语义信息并获得标识讲话者的文本。实验结果表明，我们的方法在 AISHELL-4 和 AliMeeting 数据集上相对于仅声学的说话人分离系统都有显著的改进。