Automatic emotion recognition is one of the central concerns of the
Human-Computer Interaction field as it can bridge the gap between humans and
machines. Current works train deep learning models on low-level data
representations to solve the emotion recognition task. Since emotion datasets
often have a limited amount of data, these approaches may suffer from
overfitting, and they may learn based on superficial cues. To address these
issues, we propose a novel cross-representation speech model, inspired by
disentanglement representation learning, to perform emotion recognition on
wav2vec 2.0 speech features. We also train a CNN-based model to recognize
emotions from text features extracted with Transformer-based models. We further
combine the speech-based and text-based results with a score fusion approach.
Our method is evaluated on the IEMOCAP dataset in a 4-class classification
problem, and it surpasses current works on speech-only, text-only, and
multimodal emotion recognition.

本篇研究提出一个基于深度学习和跨媒介表征学习的情感识别方法，该方法融合了文字和语音特征，并通过评估发现其在 IEMOCAP 数据集上表现优于其它方法。