Multimodal Emotion Recognition with High-level Speech and Text Features
This addresses the problem of overfitting in emotion recognition for human-computer interaction, though it is incremental as it builds on existing methods like wav2vec 2.0 and Transformers.
The paper tackled emotion recognition by proposing a cross-representation speech model and a CNN-based text model, combined with score fusion, achieving state-of-the-art results on the IEMOCAP dataset for speech-only, text-only, and multimodal tasks.
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.