  Many recent works on deep speaker embeddings train their feature extraction
networks on large classification tasks, distinguishing between all speakers in
a training set. Empirically, this has been shown to produce
speaker-discriminative embeddings, even for unseen speakers. However, it is not
clear that this is the optimal means of training embeddings that generalize
well. This work proposes two approaches to learning embeddings, based on the
notion of dropping classes during training. We demonstrate that both approaches
can yield performance gains in speaker verification tasks. The first proposed
method, DropClass, works via periodically dropping a random subset of classes
from the training data and the output layer throughout training, resulting in a
feature extractor trained on many different classification tasks. Combined with
an additive angular margin loss, this method can yield a 7.9% relative
improvement in equal error rate (EER) over a strong baseline on VoxCeleb. The
second proposed method, DropAdapt, is a means of adapting a trained model to a
set of enrolment speakers in an unsupervised manner. This is performed by
fine-tuning a model on only those classes which produce high probability
predictions when the enrolment speakers are used as input, again also dropping
the relevant rows from the output layer. This method yields a large 13.2%
relative improvement in EER on VoxCeleb. The code for this paper has been made
publicly available.
