A Pre-trained Audio-Visual Transformer for Emotion Recognition

TL;DR

This paper presents a pretrained audio-visual Transformer model trained on a large dataset for emotion recognition, demonstrating improved accuracy and robustness in low-resource scenarios.

Contribution

We introduce a novel pretrained audio-visual Transformer for emotion recognition, showing significant performance gains over training from scratch.

Findings

Fine-tuning improves emotion classification accuracy by 5-7%.

Fine-tuning increases CCC in continuous emotion recognition by 0.03-0.09.

Pretrained model performs well even with only 10% of training data.

Abstract

In this paper, we introduce a pretrained audio-visual Transformer trained on more than 500k utterances from nearly 4000 celebrities from the VoxCeleb2 dataset for human behavior understanding. The model aims to capture and extract useful information from the interactions between human facial and auditory behaviors, with application in emotion recognition. We evaluate the model performance on two datasets, namely CREMAD-D (emotion classification) and MSP-IMPROV (continuous emotion regression). Experimental results show that fine-tuning the pre-trained model helps improving emotion classification accuracy by 5-7% and Concordance Correlation Coefficients (CCC) in continuous emotion recognition by 0.03-0.09 compared to the same model trained from scratch. We also demonstrate the robustness of finetuning the pre-trained model in a low-resource setting. With only 10% of the original training set provided, fine-tuning the pre-trained model can lead to at least 10% better emotion recognition accuracy and a CCC score improvement by at least 0.1 for continuous emotion recognition.