Siamese Capsule Network for End-to-End Speaker Recognition In The Wild [article]

Amirhossein Hajavi, Ali Etemad
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
more » ... experiments to study the impact of different speaker embeddings on the Siamese capsule network. We show that the best performance is achieved by using embeddings obtained directly from the feature aggregation module of the Front-end and passing them to higher capsules using dynamic routing.
arXiv:2009.13480v1 fatcat:d36ft5jjffes3jlgjkafahbthy