Speak to me and I tell you who you are! A language-attitude study in a cultural-heritage application

Krenn, Brigitte; Schreitter, Stephanie; Neubarth, Friedrich

doi:10.1007/s00146-014-0569-0

Cited by 7 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, for the picture of the virtual robot's face appearance there were no significant differences in perceived competence, nor were they as clearly associated with a perceived nationality or gender, and the rendered faces moreover received consistent low ratings, in contrast to the greater range of scores assigned to the accented voices. These results seem to follow previous findings that accents dominate over looks for perception of ethnic or national category (Krenn et al, 2017).…”

Section: Discussionsupporting

confidence: 91%

“…Following previous research on stereotypes and interactive agents, we measured aspects related to social constructs, i.e., competence (Krenn et al, 2017), and factors used to evaluate interactive robots, i.e., likeability, to evaluate the effects of the nationality representations. From the Partner Modelling (Bartneck et al, 2009) we use the sections for Likeability and Anthropomorphism.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Stereotypical nationality representations in HRI: perspectives from international young adults

Cumbal,

Axelsson,

Mehta

et al. 2023

Front. Robot. AI

View full text Add to dashboard Cite

People often form immediate expectations about other people, or groups of people, based on visual appearance and characteristics of their voice and speech. These stereotypes, often inaccurate or overgeneralized, may translate to robots that carry human-like qualities. This study aims to explore if nationality-based preconceptions regarding appearance and accents can be found in people’s perception of a virtual and a physical social robot. In an online survey with 80 subjects evaluating different first-language-influenced accents of English and nationality-influenced human-like faces for a virtual robot, we find that accents, in particular, lead to preconceptions on perceived competence and likeability that correspond to previous findings in social science research. In a physical interaction study with 74 participants, we then studied if the perception of competence and likeability is similar after interacting with a robot portraying one of four different nationality representations from the online survey. We find that preconceptions on national stereotypes that appeared in the online survey vanish or are overshadowed by factors related to general interaction quality. We do, however, find some effects of the robot’s stereotypical alignment with the subject group, with Swedish subjects (the majority group in this study) rating the Swedish-accented robot as less competent than the international group, but, on the other hand, recalling more facts from the Swedish robot’s presentation than the international group does. In an extension in which the physical robot was replaced by a virtual robot interacting in the same scenario online, we further found the same results that preconceptions are of less importance after actual interactions, hence demonstrating that the differences in the ratings of the robot between the online survey and the interaction is not due to the interaction medium. We hence conclude that attitudes towards stereotypical national representations in HRI have a weak effect, at least for the user group included in this study (primarily educated young students in an international setting).

show abstract

Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Stereotypical nationality representations in HRI: perspectives from international young adults

Cumbal,

Axelsson,

Mehta

et al. 2023

Front. Robot. AI

View full text Add to dashboard Cite

show abstract

“…In general, this aligns with human models and stereotypes. Krenn et al [93] found that an agent with the Austrian standard accent was perceived as more educated, trustworthy, competent, polite, and serious compared to one with the dialectal accent, which was perceived as more natural, emotional, relaxed, open-minded, humorous, and aggressive. Moreover, the colloquial accent fell somewhere in between on ratings.…”

Section: Accent and Dialectmentioning

confidence: 99%

Voice in Human–Agent Interaction

Seaborn

Miyake

Pennefather³

et al. 2021

ACM Comput. Surv.

120

View full text Add to dashboard Cite

Social robots, conversational agents, voice assistants, and other embodied AI are increasingly a feature of everyday life. What connects these various types of intelligent agents is their ability to interact with people through voice. Voice is becoming an essential modality of embodiment, communication, and interaction between computer-based agents and end-users. This survey presents a meta-synthesis on agent voice in the design and experience of agents from a human-centered perspective: voice-based human--agent interaction (vHAI). Findings emphasize the social role of voice in HAI as well as circumscribe a relationship between agent voice and body, corresponding to human models of social psychology and cognition. Additionally, changes in perceptions of and reactions to agent voice over time reveals a generational shift coinciding with the commercial proliferation of mobile voice assistants. The main contributions of this work are a vHAI classification framework for voice across various agent forms, contexts, and user groups, a critical analysis grounded in key theories, and an identification of future directions for the oncoming wave of vocal machines.

show abstract

“…Namely, robots speaking with both high knowledge and high rhetorical ability were complied with more. Another study found that a synthetic agent with Austrian standard accent was perceived as possessing higher levels of education, trustworthiness, competence, politeness, and seriousness ( Krenn et al, 2017 ).…”

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

View full text Add to dashboard Cite

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.

show abstract

Speak to me and I tell you who you are! A language-attitude study in a cultural-heritage application

Cited by 7 publications

References 17 publications

Stereotypical nationality representations in HRI: perspectives from international young adults

Stereotypical nationality representations in HRI: perspectives from international young adults

Voice in Human–Agent Interaction

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

Contact Info

Product

Resources

About