# A Pre-trained Audio-Visual Transformer for Emotion Recognition Source: [A Pre-trained Audio-Visual Transformer for Emotion Recognition]() Collection index: [llms.txt]() Data updated: 2025-10-25T23:54:05.632Z Authors: Minh Tran, Mohammad Soleymani Published: 2022-01-23T03:09:16.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Emotion Recognition on CREMA-D 6-class](): Emotion Recognition; CREMA-D 6-class; WAR: 58.53. ## Original sources and implementation [Original paper]() [PDF]()