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In our multicultural world, affect-aware AI systems that support humans needthe ability to perceive affect across variations in emotion expression patternsacross cultures. These systems must perform well in cultural contexts withoutannotated affect datasets available for training models. A standard assumptionin affective computing is that affect recognition models trained and usedwithin the same culture (intracultural) will perform better than models trainedon one culture and used on different cultures (intercultural). We test thisassumption and present the first systematic study of intercultural affectrecognition models using videos of real-world dyadic interactions from sixcultures. We develop an attention-based feature selection approach undertemporal causal discovery to identify behavioral cues that can be leveraged inintercultural affect recognition models. Across all six cultures, our findingsdemonstrate that intercultural affect recognition models were as effective ormore effective than intracultural models. We identify and contribute usefulbehavioral features for intercultural affect recognition; facial features fromthe visual modality were more useful than the audio modality in this study'scontext. Our paper presents a proof-of-concept and motivation for the futuredevelopment of intercultural affect recognition systems, especially thosedeployed in low-resource situations without annotated data.

Towards Intercultural Affect Recognition: Audio-Visual Affect Recognition in the Wild Across Six Cultures