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Title
Japanese: 
English:Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition 
Author
Japanese: 井上中順, Goto Keita.  
English: Nakamasa Inoue, Keita Goto.  
Language English 
Journal/Book name
Japanese: 
English:2020 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 
Volume, Number, Page         pp. 1641-1646
Published date Dec. 31, 2020 
Publisher
Japanese: 
English:IEEE 
Conference name
Japanese: 
English:Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2020（APSIPA ASC） 
Conference site
Japanese: 
English: 
Official URL http://www.apsipa.org/proceedings/2020/APSIPA-ASC-2020.html
 
Abstract This paper introduces a semi-supervised contrastive learning framework and its application to text-independent speaker verification. The proposed framework employs general- ized contrastive loss (GCL). GCL unifies losses from two different learning frameworks, supervised metric learning and unsuper- vised contrastive learning, and thus it naturally determines the loss for semi-supervised learning. In experiments, we applied the proposed framework to text-independent speaker verification on the VoxCeleb dataset. We demonstrate that GCL enables the learning of speaker embeddings in three manners, supervised learning, semi-supervised learning, and unsupervised learning, without any changes in the definition of the loss function.

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