  With the widespread use of intelligent systems, such as smart speakers,
addressee recognition has become a concern in human-computer interaction, as
more and more people expect such systems to understand complicated social
scenes, including those outdoors, in cafeterias, and hospitals. Because
previous studies typically focused only on pre-specified tasks with limited
conversational situations such as controlling smart homes, we created a mock
dataset called Addressee Recognition in Visual Scenes with Utterances (ARVSU)
that contains a vast body of image variations in visual scenes with an
annotated utterance and a corresponding addressee for each scenario. We also
propose a multi-modal deep-learning-based model that takes different human
cues, specifically eye gazes and transcripts of an utterance corpus, into
account to predict the conversational addressee from a specific speaker's view
in various real-life conversational scenarios. To the best of our knowledge, we
are the first to introduce an end-to-end deep learning model that combines
vision and transcripts of utterance for addressee recognition. As a result, our
study suggests that future addressee recognition can reach the ability to
understand human intention in many social situations previously unexplored, and
our modality dataset is a first step in promoting research in this field.
