  The study of human emotions, traditionally a cornerstone in fields like
psychology and neuroscience, has been profoundly impacted by the advent of
artificial intelligence (AI). Multiple channels, such as speech (voice) and
facial expressions (image), are crucial in understanding human emotions.
However, AI's journey in multimodal emotion recognition (MER) is marked by
substantial technical challenges. One significant hurdle is how AI models
manage the absence of a particular modality - a frequent occurrence in
real-world situations. This study's central focus is assessing the performance
and resilience of two strategies when confronted with the lack of one modality:
a novel multimodal dynamic modality and view selection and a cross-attention
mechanism. Results on the RECOLA dataset show that dynamic selection-based
methods are a promising approach for MER. In the missing modalities scenarios,
all dynamic selection-based methods outperformed the baseline. The study
concludes by emphasizing the intricate interplay between audio and video
modalities in emotion prediction, showcasing the adaptability of dynamic
selection methods in handling missing modalities.
