  In this paper, we present a semi-supervised training technique using
pseudo-labeling for end-to-end neural diarization (EEND). The EEND system has
shown promising performance compared with traditional clustering-based methods,
especially in the case of overlapping speech. However, to get a well-tuned
model, EEND requires labeled data for all the joint speech activities of every
speaker at each time frame in a recording. In this paper, we explore a
pseudo-labeling approach that employs unlabeled data. First, we propose an
iterative pseudo-label method for EEND, which trains the model using unlabeled
data of a target condition. Then, we also propose a committee-based training
method to improve the performance of EEND. To evaluate our proposed method, we
conduct the experiments of model adaptation using labeled and unlabeled data.
Experimental results on the CALLHOME dataset show that our proposed
pseudo-label achieved a 37.4% relative diarization error rate reduction
compared to a seed model. Moreover, we analyzed the results of semi-supervised
adaptation with pseudo-labeling. We also show the effectiveness of our approach
on the third DIHARD dataset.
