A Computational Model of Culture-Specific Emotion Detection for Artificial Agents in the Learning Domain

Abstract

Nowadays, intelligent agents are expected to be affect-sensitive as agents are becoming essential entities that supports computer-mediated tasks, especially in teaching and training. These agents use common natural modalities-such as facial expressions, gestures and eye gaze in order to recognize a user's affective state and respond accordingly. However, these nonverbal cues may not be universal as emotion recognition and expression differ from culture to culture. It is important that intelligent interfaces are equipped with the abilities to meet the challenge of cultural diversity to facilitate human-machine interaction particularly in Asia. Asians are known to be more passive and possess certain traits such as indirectness and non-confrontationalism, which lead to emotions such as (culture-specific form of) shyness and timidity. Therefore, a model based on other culture may not be applicable in an Asian setting, out-rulling a one-size-fits-all approach. This study is initiated to identify the discriminative markers of culture-specific emotions based on the multimodal interactions.