  This report describes the submission from Technical University of Catalonia
(UPC) to the VoxCeleb Speaker Recognition Challenge (VoxSRC-20) at Interspeech
2020. The final submission is a combination of three systems. System-1 is an
autoencoder based approach which tries to reconstruct similar i-vectors,
whereas System-2 and -3 are Convolutional Neural Network (CNN) based siamese
architectures. The siamese networks have two and three branches, respectively,
where each branch is a CNN encoder. The double-branch siamese performs binary
classification using cross entropy loss during training. Whereas, our
triple-branch siamese is trained to learn speaker embeddings using triplet
loss. We provide results of our systems on VoxCeleb-1 test, VoxSRC-20
validation and test sets.
