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Siamese Capsule Network for End-to-End Speaker Recognition In The Wild
[article]
2020
arXiv
pre-print
We propose an end-to-end deep model for speaker verification in the wild. Our model uses thin-ResNet for extracting speaker embeddings from utterances and a Siamese capsule network and dynamic routing as the Back-end to calculate a similarity score between the embeddings. We conduct a series of experiments and comparisons on our model to state-of-the-art solutions, showing that our model outperforms all the other models using substantially less amount of training data. We also perform
arXiv:2009.13480v1
fatcat:d36ft5jjffes3jlgjkafahbthy