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

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Abstract

As the roles of social robots are increasingly diverse, it is important to design the robots according to their application fields and end-users needs. A robot’s voice is a strong social cue, the design of which can influence people’s affective evaluation and acceptance toward robots. The aim of this study is to investigate the affective evaluation of different robot voices in various application fields, and obtained suitable voice types for robots in different application fields. In particular, this study focused on the three applications (i.e., shopping reception, home companion, and education), investigated the effect of voice types (i.e., male, female, child, and synthetic) of social robots on the affective evaluation of users. Principal component analysis identified three latent influencing factors (i.e., social skills, competence and the state of the interaction relationships). Multivariable analysis proved that for overall acceptance, significant interaction effects existed between robots’ voice types and their application fields. For shopping reception robots, the most acceptable voice type is adult male voice and child voice. For home companion robots, the most acceptable robot voice types are adult male and child voices. For education robots, the most acceptable voice types are adult female and male voices. The results of this study are expected to construct design principles for robot voice design in various applications.

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Funding

This study was funded by Ministry of Science and Technology, Taiwan (107-2221-E-036-014-MY3).

Author information

Authors and Affiliations

  1. The Graduate Institute of Design Science, Tatung University, Taipei, Taiwan

    Xiao Dou

  2. Department of Industrial Design, Tatung University, Taipei, Taiwan

    Chih-Fu Wu, Kai-Chieh Lin & Tzu-Min Tseng

  3. Art College of Xiamen University, Xiamen University, Fujian, China

    Senzhong Gan

Authors
  1. Xiao Dou
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  2. Chih-Fu Wu
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  3. Kai-Chieh Lin
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  4. Senzhong Gan
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  5. Tzu-Min Tseng
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Correspondence to Chih-Fu Wu.

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Conflict of interest

Chih-fu Wu has received research grants from Ministry of Science and Technology, Taiwan. Other authors declare that they have no conflict of interest. Xiao Dou and Senzhong Gan did not receive research grants from Ministry of Science and Technology, Taiwan.

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Appendices

Appendix 1

1.1 Experimental Conversation Script

  • Conversation script in shopping reception scenario

  • Experimenter: Hello.

  • Robot: Hello, how may I assist you?

  • Experimenter: I want to buy a washing machine.

  • Robot: Sure, what type of washing machine would you like?

  • Experimenter: What are the differences between a frequency conversion washing machine and a fixed frequency washing machine?

  • B: The advantage of a frequency conversion washing machine is that it adjusts the rotation speed of the electrical motor through voltage fluctuation during the washing and spin cycles. This improves energy efficiency and quietness. Moreover, a frequency conversion washing machine can select the appropriate water flow rate, washing time, spin speed, and spin time depending on the weight and material of the clothes washed. Using a frequency conversion washing machine can therefore minimize damage to clothes and reduce the vibration, noise, and malfunction rate of the washing machine, thereby increasing the machine’s durability. By contrast, a fixed frequency washing machine is easier to use and learn. Additionally, it has a lower price compared with an inverter washing machine.

  • Experimenter: Thank you! I will think about it.

  • Robot: OK, thank you for coming!

  • Conversation script in home companion scenario

  • Experimenter: Hello.

  • Robot: Hello, how may I assist you?

  • Experimenter: I want to listen to the news.

  • B: Certainly, what type of news would you like to listen to?

  • Experimenter: Something to do with health care.

  • Robot: “Remembering to regularly wash your hands is the best approach to preventing disease. Hands can easily collect viruses and bacteria, and these can infect the body when hands make contact with the mouth, nose, and eyes. Hands can be exposed to the influenza virus when people use public transportation or enter and exit public areas. Using soap with water while washing our hands can kill viruses. Therefore, washing our hands ensures personal health and prevents the spread of infections to others.”

  • Experimenter: Thank you. Do you have anything else?

  • Robot: That is all of the latest health-related news.

  • Conversation script in education scenario

  • Experimenter: Professor, I have a question” to start the conversation.

  • Robot: Sure, what is your question?

  • Experimenter: I want to ask what the golden radio is.

  • Robot: Ok, some scholars in ancient Greece, Italy, and Germany in the past found that if you divide a line into two segments with the length of longer segment about 1.618 times that of the shorter segment, this proportion is called the golden section or the golden ratio. The concept of golden section originated from the mathematical world, but because this proportion has artistic value, many designs of art, products and buildings today would use it.

  • Experimenter: Ok, thank you.

  • Robot: You are welcome. If you have any questions, you can come to me.

Appendix 2

The sequence and number of experimental groups

Group num.

Participants num.

The sequence of laboratory rooms

The sequence of robot’ voices

R1 = Shoping reception room

R2 = Home companion room

R3 = Education room

V1 = Adult female voice robot

V2 = Adult male voice robot

V3 = Child voice robot

V4 = Synthetic voice robot

1

P1, P2

R1, R2, R3

V1, V2, V3, V4

2

P3

R1, R2, R3

V1, V2, V3, V4

3

P4, P5, P6

R1, R2, R3

V2, V1, V3, V4

4

P7, P8

R1, R2, R3

V3, V1, V2, V4

5

P9, P10

R2, R1, R3

V4, V1, V2, V3

6

P11

R2, R1, R3

V1, V2, V4, V3

7

P12

R2, R1, R3

V2, V1, V4, V3

8

P13, P14

R2, R1, R3

V3, V1, V4, V2

9

P15, P16, P17

R3, R1, R2

V4, V1, V3, V2

10

P18, P19

R3, R1, R2

V1, V3, V2, V4

11

P20

R3, R1, R2

V2, V3, V1, V4

12

P21

R3, R1, R2

V3, V2, V4, V1

13

P22, P23, P24

R3, R2, R1

V4, V2, V3, V1

14

P25

R3, R2, R1

V1, V4, V2, V3

15

P26, P27

R3, R2, R1

V2, V4, V1, V3

16

P28

R3, R2, R1

V3, V4, V1, V2

17

P29, P30

R1, R3, R2

V4, V3, V2, V1

18

P31

R1, R3, R2

V1, V4, V3, V2

19

P32, P33

R1, R3, R2

V2, V4, V3, V1

20

P34

R1, R3, R2

V3, V4, V2, V1

21

P35

R1, R3, R2

V4, V3, V2, V1

22

P36

R2, R3, R1

V1, V2, V3, V4

23

P37, P38

R2, R3, R1

V2, V1, V3, V4

24

P39

R2, R3, R1

V3, V1, V2, V4

25

P40, P41, P42

R2, R3, R1

V4, V1, V2, V3

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Dou, X., Wu, CF., Lin, KC. et al. Effects of Different Types of Social Robot Voices on Affective Evaluations in Different Application Fields. Int J of Soc Robotics 13, 615–628 (2021). https://doi.org/10.1007/s12369-020-00654-9

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