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Graph Convolutional Network Based Semi-Supervised Learning on Multi-Speaker Meeting Data
[article]
2022
arXiv
pre-print
Unsupervised clustering on speakers is becoming increasingly important for its potential uses in semi-supervised learning. In reality, we are often presented with enormous amounts of unlabeled data from multi-party meetings and discussions. An effective unsupervised clustering approach would allow us to significantly increase the amount of training data without additional costs for annotations. Recently, methods based on graph convolutional networks (GCN) have received growing attention for
arXiv:2204.11501v1
fatcat:4hjc5rgvxnhrjovhwpc4etjdau