Exploring Speaker-Related Information in Spoken Language Understanding
  for Better Speaker Diarization
Luyao Cheng Siqi Zheng Zhang Qinglin Hui Wang Yafeng Chen Qian Chen
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.