  Graph representation learning has become a hot research topic due to its
powerful nonlinear fitting capability in extracting representative node
embeddings. However, for sequential data such as speech signals, most
traditional methods merely focus on the static graph created within a sequence,
and largely overlook the intrinsic evolving patterns of these data. This may
reduce the efficiency of graph representation learning for sequential data. For
this reason, we propose an adaptive graph representation learning method based
on dynamically evolved graphs, which are consecutively constructed on a series
of subsequences segmented by a sliding window. In doing this, it is better to
capture local and global context information within a long sequence. Moreover,
we introduce a weighted approach to update the node representation rather than
the conventional average one, where the weights are calculated by a novel
matrix computation based on the degree of neighboring nodes. Finally, we
construct a learnable graph convolutional layer that combines the graph
structure loss and classification loss to optimize the graph structure. To
verify the effectiveness of the proposed method, we conducted experiments for
speech emotion recognition on the IEMOCAP and RAVDESS datasets. Experimental
results show that the proposed method outperforms the latest (non-)graph-based
models.
