  This paper proposes a novel formulation of prototypical loss with mixup for
speaker verification. Mixup is a simple yet efficient data augmentation
technique that fabricates a weighted combination of random data point and label
pairs for deep neural network training. Mixup has attracted increasing
attention due to its ability to improve robustness and generalization of deep
neural networks. Although mixup has shown success in diverse domains, most
applications have centered around closed-set classification tasks. In this
work, we propose contrastive-mixup, a novel augmentation strategy that learns
distinguishing representations based on a distance metric. During training,
mixup operations generate convex interpolations of both inputs and virtual
labels. Moreover, we have reformulated the prototypical loss function such that
mixup is enabled on metric learning objectives. To demonstrate its
generalization given limited training data, we conduct experiments by varying
the number of available utterances from each speaker in the VoxCeleb database.
Experimental results show that applying contrastive-mixup outperforms the
existing baseline, reducing error rate by 16% relatively, especially when the
number of training utterances per speaker is limited.
