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arXiv · 2112.12343

Graph attentive feature aggregation for text-independent speaker verification

Hye-jin Shim·Jungwoo Heo·Jae-han Park·Ga-hui Lee·Ha-Jin Yu
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Abstract

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.

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Hye-jin Shim, Jungwoo Heo, Jae-han Park, Ga-hui Lee, Ha-Jin Yu. 2021-12-23. Graph attentive feature aggregation for text-independent speaker verification. https://arxiv.org/abs/2112.12343

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