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Recognition of social signals, from human facial expressions or prosody ofspeech, is a popular research topic in human-robot interaction studies. Thereis also a long line of research in the spoken dialogue community thatinvestigates user satisfaction in relation to dialogue characteristics.However, very little research relates a combination of multimodal socialsignals and language features detected during spoken face-to-face human-robotinteraction to the resulting user perception of a robot. In this paper we showhow different emotional facial expressions of human users, in combination withprosodic characteristics of human speech and features of human-robot dialogue,correlate with users' impressions of the robot after a conversation. We findthat happiness in the user's recognised facial expression strongly correlateswith likeability of a robot, while dialogue-related features (such as number ofhuman turns or number of sentences per robot utterance) correlate withperceiving a robot as intelligent. In addition, we show that facial expression,emotional features, and prosody are better predictors of human ratings relatedto perceived robot likeability and anthropomorphism, while linguistic andnon-linguistic features more often predict perceived robot intelligence andinterpretability. As such, these characteristics may in future be used as anonline reward signal for in-situ Reinforcement Learning based adaptivehuman-robot dialogue systems.

Sympathy Begins with a Smile, Intelligence Begins with a Word: Use of Multimodal Features in Spoken Human-Robot Interaction