Effects of Different Types of Social Robot Voices on Affective Evaluations in Different Application Fields

Dou, Xiao; Wu, Chih‐Fu; Lin, Kuo‐Cherng; Gan, Senzhong; Tseng, Tzu‐Min

doi:10.1007/s12369-020-00654-9

Cited by 31 publications

(19 citation statements)

References 54 publications

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“…Tay et al [176] found that a healthcare robot with a feminine, extraverted voice and a security robot with a masculine, introverted voice were rated highly, although gender was not as strong as personality in terms of role matching. In contrast, Dou et al [44] found that masculine agents were considered more suitable for shopping and the home, while feminine and masculine voices were rated appropriate for education.…”

Section: Gendermentioning

confidence: 90%

“…Whether or not gender matters appears to depend on when the study was conducted, in line with the vocaloid shift, as well as other factors that we could not determine in this survey. For example, early research showed gender stereotyping and preferences based on human social norms (e.g., Reference [134]), while recent research suggests an influence of the feminine-gendered voice assistants (e.g., Reference [24]) as well as the emergence of other influential factors, like personality [176], and changing expectations about gender (e.g., Reference [44]). The influence of commercial voice assistants is further bolstered by the results of research on very young children (e.g., Reference [157]).…”

Section: Empiricalmentioning

confidence: 99%

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Voice in Human–Agent Interaction

Seaborn

Miyake

Pennefather³

et al. 2021

ACM Comput. Surv.

103

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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.

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Section: Gendermentioning

confidence: 90%

Section: Empiricalmentioning

confidence: 99%

Voice in Human–Agent Interaction

Seaborn

Miyake

Pennefather³

et al. 2021

ACM Comput. Surv.

103

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“…In the process of speech recognition, the maximum likelihood probability of speech recognition and hidden Markov model parameters can be calculated, and then the best recognition results can be output. Its recognition pattern library is the best pattern parameter with matching pattern and pre-stored pattern sample obtained by training, which is a centrifugal speech recognition model [14][15][16][17].…”

Section: Design Of Ai Voice Interaction System For An Educational Robotmentioning

confidence: 99%

Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot

Yang

Wang

2020

Sustainability

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In order to promote the development of individualized, accurate and intelligent physical education teaching, combined with artificial intelligence technology, the current physical education teaching mode has been improved. Through the establishment of an artificial intelligence educational robot based on voice interaction, a hybrid physical education teaching mode is constructed to realize personalized education for students. First, the speech recognition system is designed from three aspects of speech recognition, interaction management and speech synthesis, and the accuracy of recognition is improved by algorithm. Second, a new mode of hybrid physical education teaching is constructed. Through intelligent information technology, the advantages of traditional physical education teaching are combined to improve the classroom efficiency of physical education teaching and personalized education ability for students. Finally, the relevant experimental scheme and questionnaire are designed, and the actual situation of an educational robot introduced into physical education teaching is investigated and evaluated. The results show that the recognition accuracy of the artificial intelligence speech recognition system can reach more than 90%. It can communicate well with students and answer students’ questions. An educational robot is introduced into physical education teaching, and students’ learning attitude and interest are evaluated. The results show that before and after the introduction of an educational robot in physical education teaching, the average score of students’ learning interest increases by 21 points, and the average score of learning attitude increases by 9.8 points. Therefore, the introduction of an artificial intelligence educational robot based on voice interaction in physical education teaching can help to improve the classroom efficiency of physical education teaching and students’ interest. This study provides a reference for the development of artificial intelligence teaching and promoting the development of artificial intelligence.

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“…Regarding facial expressions, mechanical and humanoid robots do not have complete facial expression functionality; expressive light is typically used as a substitute for facial expressions ( Collins et al, 2015 ; Baraka et al, 2015 , 2016 ; Baraka and Veloso, 2018 ; Song and Yamada, 2018 ; Westhoven et al, 2019 ). Our previous works have comprehensively discussed the use of voice and expressive light in social robots, providing a functionality reference for applications in three occupational fields: shopping reception, home companion, and education ( Dou et al, 2020a , b , 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“…In our previous study ( Dou et al, 2021 ), we have confirmed that the voices and expressive lights of social robots enable the perception of distinct personality traits. Moreover, the optimal configuration of voice and lighting design parameters for social robots applied in the four fields of interest has been determined ( Dou et al, 2020a , b , 2021 ). New discoveries on robot gestures are also presented herein.…”

Section: Introductionmentioning

confidence: 99%

Designing Gestures of Robots in Specific Fields for Different Perceived Personality Traits

Niu

Wu²,

Dou³

et al. 2022

Front. Psychol.

Self Cite

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Since their development, social robots have been a popular topic of research, with numerous studies evaluating their functionality or task performance. In recent years, social robots have begun to be regarded as social actors at work, and their social attributes have been explored. Therefore, this study focused on four occupational fields (shopping reception, home companion, education, and security) where robots are widely used, exploring the influence of robot gestures on their perceived personality traits and comparing the gesture design guidelines required in specific occupational fields. The study was conducted in two stages. In the first stage, an interactive script was developed; moreover, observation was employed to derive gestures related to the discourse on the fields of interest. The second stage involved robot experimentation based on human–robot interaction through video. Results show that metaphoric gestures appeared less frequently than did deictic, iconic, or beat gestures. Robots’ perceived personality traits were categorized into sociality, competence, and status. Introducing all types of gestures helped enhance perceived sociality. The addition of deictic, and iconic gestures significantly improved perceived competence and perceived status. Regarding the shopping reception robot, after the inclusion of basic deictic and iconic gestures, sufficient beats gestures should be implemented to create a friendly and outgoing demeanor, thereby promoting user acceptance. In the home companion, education, and security contexts, the addition of beat gestures did not affect the overall acceptance level; the designs should instead be focused on the integration of the other gesture types.

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Effects of Different Types of Social Robot Voices on Affective Evaluations in Different Application Fields

Cited by 31 publications

References 54 publications

Voice in Human–Agent Interaction

Voice in Human–Agent Interaction

Hybrid Physical Education Teaching and Curriculum Design Based on a Voice Interactive Artificial Intelligence Educational Robot

Designing Gestures of Robots in Specific Fields for Different Perceived Personality Traits

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