  Speech emotion recognition (SER) is an essential part of human-computer
interaction. In this paper, we propose an SER network based on a Graph
Isomorphism Network with Weighted Multiple Aggregators (WMA-GIN), which can
effectively handle the problem of information confusion when neighbour nodes'
features are aggregated together in GIN structure. Moreover, a Full-Adjacent
(FA) layer is adopted for alleviating the over-squashing problem, which is
existed in all Graph Neural Network (GNN) structures, including GIN.
Furthermore, a multi-phase attention mechanism and multi-loss training strategy
are employed to avoid missing the useful emotional information in the stacked
WMA-GIN layers. We evaluated the performance of our proposed WMA-GIN on the
popular IEMOCAP dataset. The experimental results show that WMA-GIN outperforms
other GNN-based methods and is comparable to some advanced non-graph-based
methods by achieving 72.48% of weighted accuracy (WA) and 67.72% of unweighted
accuracy (UA).
