Designing Social Robots' Speech in the Hotel Context - A Series of Online Studies

Steinhaeusser, Sophia C.; Lein, Martina; Donnermann, Melissa; Lugrin, Birgit

doi:10.1109/ro-man53752.2022.9900668

Cited by 4 publications

(1 citation statement)

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“…Importantly, in Switzerland, a dialect serves strongly as a means of social demarcation. However, a most recent study with a service robot Pepper in a hotel context showed that using the local dialect did not affect robot acceptance and attitudes ( Steinhaeusser et al, 2022 ). The study was conducted online and participants speaking the Flanconian dialect vs. standard German were randomly assigned to the dialect or standard language conditions.…”

Section: Introductionmentioning

confidence: 99%

“Ick bin een Berlina”: dialect proficiency impacts a robot’s trustworthiness and competence evaluation

Kühne,

Herbold,

Bendel

et al. 2024

Front. Robot. AI

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Background: Robots are increasingly used as interaction partners with humans. Social robots are designed to follow expected behavioral norms when engaging with humans and are available with different voices and even accents. Some studies suggest that people prefer robots to speak in the user’s dialect, while others indicate a preference for different dialects.Methods: Our study examined the impact of the Berlin dialect on perceived trustworthiness and competence of a robot. One hundred and twenty German native speakers (Mage = 32 years, SD = 12 years) watched an online video featuring a NAO robot speaking either in the Berlin dialect or standard German and assessed its trustworthiness and competence.Results: We found a positive relationship between participants’ self-reported Berlin dialect proficiency and trustworthiness in the dialect-speaking robot. Only when controlled for demographic factors, there was a positive association between participants’ dialect proficiency, dialect performance and their assessment of robot’s competence for the standard German-speaking robot. Participants’ age, gender, length of residency in Berlin, and device used to respond also influenced assessments. Finally, the robot’s competence positively predicted its trustworthiness.Discussion: Our results inform the design of social robots and emphasize the importance of device control in online experiments.

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Section: Introductionmentioning

confidence: 99%