  Most neural speaker diarization systems rely on sufficient manual training
data labels, which are hard to collect under real-world scenarios. This paper
proposes a semi-supervised speaker diarization system to utilize large-scale
multi-channel training data by generating pseudo-labels for unlabeled data.
Furthermore, we introduce cross-channel attention into the Neural Speaker
Diarization Using Memory-Aware Multi-Speaker Embedding (NSD-MA-MSE) to learn
channel contextual information of speaker embeddings better. Experimental
results on the CHiME-7 Mixer6 dataset which only contains partial speakers'
labels of the training set, show that our system achieved 57.01% relative DER
reduction compared to the clustering-based model on the development set. We
further conducted experiments on the CHiME-6 dataset to simulate the scenario
of missing partial training set labels. When using 80% and 50% labeled training
data, our system performs comparably to the results obtained using 100% labeled
data for training.
