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Multimodal emotion recognition (MER) in practical scenarios presents asignificant challenge due to the presence of incomplete data, such as missingor noisy data. Traditional methods often discard missing data or replace itwith a zero vector, neglecting the availability issue of noisy data.Consequently, these approaches are not fully applicable to realistic scenarios,where both missing and noisy data are prevalent. To address this problem, wepropose a novel noise-robust MER model, named NMER, which effectively learnsrobust multimodal joint representations from incomplete data containing noise.Our approach incorporates two key components. First, we introduce a noisescheduler that adjusts the type and level of noise in the training data,emulating the characteristics of incomplete data in realistic scenarios.Second, we employ a Variational AutoEncoder (VAE)-based NMER model to generaterobust multimodal joint representations from the noisy data, leveraging themodality invariant feature. The experimental results on the benchmark datasetIEMOCAP indicate the proposed NMER outperforms state-of-the-art MER systems.The ablation results also confirm the effectiveness of the VAE structure. Werelease our code at \href{https://github.com/WooyoohL/Noise-robust_MER.

Learning Noise-Robust Joint Representation for Multimodal Emotion Recognition under Realistic Incomplete Data Scenarios