Graph-based multi-Feature fusion method for speech emotion recognition

TL;DR

This paper introduces a graph-based multi-feature fusion method for speech emotion recognition that explicitly models relationships between features, leading to improved cross-corpus performance and better emotion recognition accuracy.

Contribution

It proposes a novel graph-based fusion approach with multi-dimensional edge features to better capture feature interactions in speech emotion recognition.

Findings

Achieved 17.28% improvement in CCC scores for arousal on German data.

Demonstrated 13% performance improvement over existing fusion techniques.

Validated effectiveness on SEWA and AVEC 2019 datasets.

Abstract

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.