Backchannels, i.e., short interjections by an interlocutor to indicate attention, understanding or agreement regarding utterances by another conversation participant, are fundamental in human-human interaction. Lack of backchannels or if they have unexpected timing or formulation may influence the conversation negatively, as misinterpretations regarding attention, understanding or agreement may occur. However, several studies over the years have shown that there may be cultural differences in how backchannels are provided and perceived and that these differences may affect intercultural conversations. Culturally aware robots must hence be endowed with the capability to detect and adapt to the way these conversational markers are used across different cultures. Traditionally, culture has been defined in terms of nationality, but this is more and more considered to be a stereotypic simplification. We therefore investigate several socio-cultural factors, such as the participants' gender, age, first language, extroversion and familiarity with robots, that may be relevant for the perception of backchannels. We first cover existing research on cultural influence on backchannel formulation and perception in human-human interaction and on backchannel implementation in Human-Robot Interaction. We then present an experiment on second language spoken practice, in which we investigate how backchannels from the social robot Furhat influence interaction (investigated through speaking time ratios and ethnomethodology and multimodal conversation analysis) and impression of the robot (measured by post-session ratings). The experiment, made in a triad word game setting, is focused on if activity-adaptive robot backchannels may redistribute the participants' speaking time ratio, and/or if the participants' assessment of the robot is influenced by the backchannel strategy. The goal is to explore how robot backchannels should be adapted to different language learners to encourage their participation while being perceived as socio-culturally appropriate. We find that a strategy that displays more backchannels towards a less active speaker may substantially decrease the difference in speaking time between the two speakers, that different socio-cultural groups respond differently to the robot's backchannel strategy and that they also perceive the robot differently after the session. We conclude that the robot may need different backchanneling strategies towards speakers from different socio-cultural groups in order to encourage them to speak and have a positive perception of the robot.
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http://dx.doi.org/10.3389/frobt.2023.988042 | DOI Listing |
Front Neurosci
February 2024
Department of Otorhinolaryngology/Head and Neck Surgery, University Medical Center Groningen, University of Groningen, Groningen, Netherlands.
Introduction: Underlying mechanisms of speech perception masked by background speakers, a common daily listening condition, are often investigated using various and lengthy psychophysical tests. The presence of a social agent, such as an interactive humanoid NAO robot, may help maintain engagement and attention. However, such robots potentially have limited sound quality or processing speed.
View Article and Find Full Text PDFFront Robot AI
January 2023
Department of Education, Stockholm University, Stockholm, Sweden.
Backchannels, i.e., short interjections by an interlocutor to indicate attention, understanding or agreement regarding utterances by another conversation participant, are fundamental in human-human interaction.
View Article and Find Full Text PDFFront Psychol
December 2020
Department of Computer Science, Yale University, New Haven, CT, United States.
As teams of people increasingly incorporate robot members, it is essential to consider how a robot's actions may influence the team's social dynamics and interactions. In this work, we investigated the effects of verbal support from a robot (e.g.
View Article and Find Full Text PDFBiomed Sci Instrum
February 2016
A Working Hypothesis, Inc.
Unlabelled: In this paper the author extends his computerized human nervous system function emulator (HNSFE) to include elements of courtship, pair bonding, erotic stimulation and sexual intercourse, producing a human sexual function emulator (HSFE). The HNSFE is a biologically-inspired, open systems, multitasking, multiprocessor, IEEE 1275 program which imitates many neural-cognitive operations of the human brain. It is the control element of the authors robotic Android with Neural Network, Intellect and Emotions (ANNIE).
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