  Exploring proper way to conduct multi-speech feature fusion for cross-corpus
speech emotion recognition is crucial as different speech features could
provide complementary cues reflecting human emotion status. While most previous
approaches only extract a single speech feature for emotion recognition,
existing fusion methods such as concatenation, parallel connection, and
splicing ignore heterogeneous patterns in the interaction between features and
features, resulting in performance of existing systems. In this paper, we
propose a novel graph-based fusion method to explicitly model the relationships
between every pair of speech features. Specifically, we propose a
multi-dimensional edge features learning strategy called Graph-based
multi-Feature fusion method for speech emotion recognition. It represents each
speech feature as a node and learns multi-dimensional edge features to
explicitly describe the relationship between each feature-feature pair in the
context of emotion recognition. This way, the learned multi-dimensional edge
features encode speech feature-level information from both the vertex and edge
dimensions. Our Approach consists of three modules: an Audio Feature
Generation(AFG)module, an Audio-Feature Multi-dimensional Edge Feature(AMEF)
module and a Speech Emotion Recognition (SER) module. The proposed methodology
yielded satisfactory outcomes on the SEWA dataset. Furthermore, the method
demonstrated enhanced performance compared to the baseline in the AVEC 2019
Workshop and Challenge. We used data from two cultures as our training and
validation sets: two cultures containing German and Hungarian on the SEWA
dataset, the CCC scores for German are improved by 17.28% for arousal and 7.93%
for liking. The outcomes of our methodology demonstrate a 13% improvement over
alternative fusion techniques, including those employing one dimensional
edge-based feature fusion approach.
