  The goal of this paper is text-independent speaker verification where
utterances come from 'in the wild' videos and may contain irrelevant signal.
While speaker verification is naturally a pair-wise problem, existing methods
to produce the speaker embeddings are instance-wise. In this paper, we propose
Cross Attentive Pooling (CAP) that utilizes the context information across the
reference-query pair to generate utterance-level embeddings that contain the
most discriminative information for the pair-wise matching problem. Experiments
are performed on the VoxCeleb dataset in which our method outperforms
comparable pooling strategies.
