Deep learning based multi-modal addressee recognition in visual scenes with utterances
Thao Le Minh (Tokyo Institute of Technology), Nobuyuki Shimizu, Takashi Miyazaki, Koichi Shinoda (Tokyo Institute of Technology)
The 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence(IJCAI-ECAI 2018), 2018/7
自然言語処理 (Natural Language Processing) 画像処理 (Image Processing) 機械学習 (Machine Learning) 次世代UI・インタラクション (HCI)
- 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.
Deep learning based multi-modal addressee recognition in visual scenes with utterances（外部サイト／External Site Link）