  This work presents a novel back-end framework for speaker verification using
graph attention networks. Segment-wise speaker embeddings extracted from
multiple crops within an utterance are interpreted as node representations of a
graph. The proposed framework inputs segment-wise speaker embeddings from an
enrollment and a test utterance and directly outputs a similarity score. We
first construct a graph using segment-wise speaker embeddings and then input
these to graph attention networks. After a few graph attention layers with
residual connections, each node is projected into a one-dimensional space using
affine transform, followed by a readout operation resulting in a scalar
similarity score. To enable successful adaptation for speaker verification, we
propose techniques such as separating trainable weights for attention map
calculations between segment-wise speaker embeddings from different utterances.
The effectiveness of the proposed framework is validated using three different
speaker embedding extractors trained with different architectures and objective
functions. Experimental results demonstrate consistent improvement over various
baseline back-end classifiers, with an average equal error rate improvement of
20% over the cosine similarity back-end without test time augmentation.
