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

## TL;DR

This study explores how multimodal social signals and dialogue features during face-to-face human-robot interactions influence user perceptions, revealing specific emotional and dialogue cues that correlate with robot likeability and intelligence.

## Contribution

It demonstrates the relationship between facial expressions, prosody, dialogue features, and user perceptions, highlighting potential online reward signals for adaptive dialogue systems.

## Key findings

- Happiness correlates with robot likeability.
- Dialogue features relate to perceived robot intelligence.
- Facial and prosodic features predict robot likeability and anthropomorphism.

## Abstract

Recognition of social signals, from human facial expressions or prosody of speech, is a popular research topic in human-robot interaction studies. There is also a long line of research in the spoken dialogue community that investigates user satisfaction in relation to dialogue characteristics. However, very little research relates a combination of multimodal social signals and language features detected during spoken face-to-face human-robot interaction to the resulting user perception of a robot. In this paper we show how different emotional facial expressions of human users, in combination with prosodic characteristics of human speech and features of human-robot dialogue, correlate with users' impressions of the robot after a conversation. We find that happiness in the user's recognised facial expression strongly correlates with likeability of a robot, while dialogue-related features (such as number of human turns or number of sentences per robot utterance) correlate with perceiving a robot as intelligent. In addition, we show that facial expression, emotional features, and prosody are better predictors of human ratings related to perceived robot likeability and anthropomorphism, while linguistic and non-linguistic features more often predict perceived robot intelligence and interpretability. As such, these characteristics may in future be used as an online reward signal for in-situ Reinforcement Learning based adaptive human-robot dialogue systems.