  Research in speaker recognition has recently seen significant progress due to
the application of neural network models and the availability of new
large-scale datasets. There has been a plethora of work in search for more
powerful architectures or loss functions suitable for the task, but these works
do not consider what information is learnt by the models, apart from being able
to predict the given labels.
  In this work, we introduce an environment adversarial training framework in
which the network can effectively learn speaker-discriminative and
environment-invariant embeddings without explicit domain shift during training.
We achieve this by utilising the previously unused `video' information in the
VoxCeleb dataset. The environment adversarial training allows the network to
generalise better to unseen conditions. The method is evaluated on both speaker
identification and verification tasks using the VoxCeleb dataset, on which we
demonstrate significant performance improvements over baselines.
