  We train an identity verification architecture and evaluate modifications to
the part of the model that combines audio and visual representations, including
in scenarios where one input is missing in either of two examples to be
compared. We report results on the Voxceleb1-E test set that suggest averaging
the output embeddings improves error rate in the full-modality setting and when
a single modality is missing, and makes more complete use of the embedding
space than systems which use shared layers and discuss possible reasons for
this behavior.
