As a young and emerging field in social human–robot interaction (HRI), semantic-free utterances (SFUs) research has been receiving attention over the last decade. SFUs are an auditory interaction means for machines that allow emotion and intent expression, which are composed of vocalizations and sounds without semantic content or language dependence. Currently, SFUs are most commonly utilized in animation movies (e.g., R2-D2, WALL-E, Despicable Me), cartoons (e.g., “Teletubbies,” “Morph,” “La Linea”), and computer games (e.g., The Sims) and hold significant potential for applications in HRI. SFUs are categorized under four general types: Gibberish Speech (GS), Non-Linguistic Utterances (NLUs), Musical Utterances (MU), and Paralinguistic Utterances (PU). By introducing the concept of SFUs and bringing multiple sets of studies in social HRI that have never been analyzed jointly before, this article addresses the need for a comprehensive study of the existing literature for SFUs. It outlines the current grand challenges, open questions, and provides guidelines for future researchers considering to utilize SFU in social HRI.

No abstract available.

This paper presents an experiment investigating the influence that a situational context has upon how people affectively interpret Non-Linguistic Utterances made by a social robot. Subjects were presented five video conditions showing the robot making both a positive and negative utterance, the robot being subject to an action (e.g. receiving a kiss, or a slap), and then two videos showing the combination of the action and the robot reacting with both the positive and negative utterances. For each video an affective rating of valence was provided based upon how the subjects thought the robot felt given what had happened in the video. This was repeated for 5 different action scenarios. Results show that the affective interpretation of an action appears to override that of an utterance, regardless of the affective charge of the utterance. Furthermore, it is shown that if the meaning of the action and utterance are aligned, the overall interpretation is amplified. These findings are considered with respect to the practical use of utterances during social HRI.

This paper presents the results of a small experiment aimed at determining whether people are comfortable with a social robot that uses robotic Non-Linguistic Utterances alongside Natural Language, rather than as a replacement. The results suggest that while people have the most preference for a robot that uses only natural language, a robot that combines NLUs and natural language is seen as more preferable than a robot that only employes NLUs. This suggests that there is potential for NLUs to be used in combination with natural language. In light of this, potential utilities and motivations for using NLUs in such a manner are outlined.

Vocal affective displays are vital for achieving engaging and effective Human-Robot Interaction. The same can be said for linguistic interaction also, however, while emphasis may be placed upon linguistic interaction, there are also inherent risks: users are bound to a single language, and breakdowns are frequent due to current technical limitations. This work explores the potential of non-linguistic utterances. A recent study is briefly outlined in which school children were asked to rate a variety of non-linguistic utterances on an affective level using a facial gesture tool. Results suggest, for example, that utterance rhythm may be an influential independent factor, whilst the pitch contour of an utterance may have little importance. Also evidence for categorical perception of emotions is presented, an issue that may impact important areas of HRI away from vocal displays of affect.