  The objective of this paper is to combine multiple frame-level features into
a single utterance-level representation considering pairwise relationship. For
this purpose, we propose a novel graph attentive feature aggregation module by
interpreting each frame-level feature as a node of a graph. The
inter-relationship between all possible pairs of features, typically exploited
indirectly, can be directly modeled using a graph. The module comprises a graph
attention layer and a graph pooling layer followed by a readout operation. The
graph attention layer first models the non-Euclidean data manifold between
different nodes. Then, the graph pooling layer discards less informative nodes
considering the significance of the nodes. Finally, the readout operation
combines the remaining nodes into a single representation. We employ two recent
systems, SE-ResNet and RawNet2, with different input features and architectures
and demonstrate that the proposed feature aggregation module consistently shows
a relative improvement over 10%, compared to the baseline.
