s Voice to Make Human-Robot Interaction More Engaging

Using a Robot's Voice to Make Human-Robot Interaction 

More Engaging 

Hans van de Kamp 

University of Twente 

P.O. Box 217, 7500AE Enschede 

The Netherlands 

h.vandekamp@student.utwente.nl 

ABSTRACT 

Nowadays a robot is becoming more than just a machine, the 

robot becomes an interaction partner. A human needs to be 

engaged to interact with the robot. This paper is about an 

experiment on robot voices in a task-based environment. The 

goal was to determine the influence of the robot's voice on the 

way humans are engaged or interested to perform a certain 

task. This research is contributing to the topic of engagement 

in human-robot interaction with different voice styles. The 

participant is asked to perform six small assignments to 

measure the effects of the different voices; a human-like voice 

(N=10) and a machine-like or mechanical voice (N=11). 

There were some significant differences between the two 

voices, mostly related to the likeability of the robot. The 

differences between the voices in terms of interest or 

engagement turned out to be minimal and not significant. 

Keywords 

Robot voice, challenging, engagement, human-robot 

interaction, task interest, robot interest 

1. INTRODUCTION 

Different frameworks for human-robot interaction have been 

created in the past years [1]. Most of them are trying to 

improve human-robot interaction by incorporating human 

behavior and human personality traits in robots [7, 16]. Voice 

is an important factor in human personality [8], therefore 

many robots use a human-like voice to interact with humans. 

Creating a human-like interaction partner has proven to be 

valuable in human-robot interaction [4, 10] in terms of 

effectiveness and efficiency [11, 15]. This research will not 

evaluate human-robot interaction by measuring task 

effectiveness or task efficiency, but it will evaluate humanrobot 

interaction by the way humans are engaged [12] while 

performing a certain assignment. This research aims to 

provide insight on the relationship between a robot’s voice 

and engagement [12] in terms of interest. 

Permission to make digital or hard copies of all or part of this work for 

personal or classroom use is granted without fee provided that copies are 

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18 th Twente Student Conference on IT, January 25 th , 2013, Enschede, The 

Netherlands. 

Copyright 2013, University of Twente, Faculty of Electrical Engineering, 

Mathematics and Computer Science. 

1.1 Problem statement 

Other research [4] focuses on models in which robots mirror 

the participants’ behavior to create a more human-like 

interaction partner. However, most of that research does not 

focus on engaging a human. This paper focuses only on 

engaging a participant in human-robot interaction. Engaging 

is related to the concept of interest as it describes attentional 

and emotional involvement [12]. The goal of this research is 

to determine the influence of a robot’s voice on the level of 

engagement in terms of interest [12] in human-robot 

interaction. 

It seems normal to use a more human-like robot to improve 

human-robot interaction, assuming that the robot is perceived 

as human-like. However, it is a fact that most robots are far 

from being like a human. If a human perceives the robot as 

artificial and not as human-like, it might influence the 

expectancy of the robot voice. 

If a robot is perceived as artificial and uses a mechanical voice 

instead of the human-like voice, does this influence the degree 

of which humans are engaged to perform a certain task Are 

humans more interested in a robot with a mechanical voice 

instead of a human-like voice 

Both questions are important to understand the relationship 

between humans and robots in human-robot interaction. Or in 

this paper more specific: the relationship between voice and 

engagement. With the previous questions in mind, the 

following research questions can be created. 

1.2 Research Questions 

To investigate the relationship between a robot’s voice and 

participant interest towards the robot or task a few questions 

need to be answered. The main research question is: 

Does a robot with a humanlike voice make human-robot 

interaction more engaging than a mechanical voice 

Other research questions will be used to find an answer to the 

main question: 

- How can we find out if a person is engaged or 

interested in the robot or task while interacting with 

a robot 

- Does a robot’s voice influence the way participants 

are engaged to perform a task in terms of interest 

Which can be divided into two more questions: 

o 

o 

Does a robot’s voice influence the way humans 

are interested in the robot 

Does a robot’s voice influence the way humans 

are interested in the task

2. RELATED WORK 

Research on human-robot interaction is often about creating a 

lifelike interaction partner. Therefore a lot of research is 

conducted with a human-like voice only, of which some are 

mentioned in the introduction. Important related work to this 

research is conducted by Walters et al. Rich and Sidner also 

wrote about the concept of engagement in human-robot 

interaction. However, they did not use different voice styles or 

robot appearances like Walters et al. 

A paper on robot appearance and personality by Walters et al. 

[17] investigated people’s perceptions of different robot 

appearances. The research uses the definitions of robot 

appearance based on the definitions of Gong and Nass [5] and 

MacDorman and Ishiguro [9]. In this experiment the robot 

appearances are referred to as human-like or machine-like. 

This paper describes the relation between interest and robot 

voices, it does not talk about appearance preferences in 

general. 

years with an average of 21,6 years. The second oldest 

participant was 24 years old. 

The experiment using the human voice had 10 participants 

and the experiment using the robot voice had 11. 20 of those 

participants were students of exact sciences such as Computer 

Science, Electrical Engineering, Mathematics or a master in 

the same area. The other student studied Climate & 

management and was apparently lost.. Because of the large 

amount of students with a technical background only 4 

participants were female. They were equally divided over 

both conditions. 

Other research by Walters et al. [18] used different robot 

voices similar to the experiment described in this paper. 

There has also been research on the gender of the robot. 

Siegel et al. [13] conducted research to determine the 

preferences of males and females on robot gender. The 

research showed that females generally did not have a 

preference of robot gender. However, males seem to have a 

preference of a female robot gender. 

Research performed by Crowell et al. [3] indicate that the 

perceived gender of the robot may influence human sexrelated 

characteristics. 

3. METHODOLOGY 

In this experiment the Magabot robot is used. A photo of the 

robot is shown in the figure at the right. The robot is small 

(less than 1 meter tall) and offers a platform on which a laptop 

can be placed. 

The robot will be controlled using a Wizard of Oz technique 

because of communication problems between the robot and 

Flash. The robot will drive on a platform between two tables, 

which will be explained in section 3.3. The voice and eyes are 

programmed in Flash (ActionScript). The robot used a 

predefined script in the form of a Flash timeline. The timeline 

was divided into several segments to allow a researcher to 

control the robot's script. 

As seen in the figure at the right, the laptop displayed the 

robot's eyes. The robot eyes and effects such as blinking were 

used to make the robot more life-like. In the experiment the 

robot eyes were used to look at objects. 

Based on the research conducted by Siegel et al. [13], the 

robot had a female voice. Both the human-like and machinelike 

voice were female (or female-like) voices. The used 

voices are discussed in section 3.2. The robot itself had no 

gender. The gender was determined by the voice of the robot. 

The robot introduced herself as Jane. 

3.1 Participants 

A total of 21 participants took part in the experiment using a 

between subjects design. Their age varied between 19 and 31 

Figure 1: the Magabot with laptop and eyes used in the 

experiment. 

3.2 Voice 

Two different female voices were used in the experiment. The 

robot used a predefined script (in English) to communicate 

with the participants. Both voices were created with the 

MARY Text-to-Speech system. The first group of participants 

(N=10) interacted with a robot using a synthesized human-like 

female voice. The second group of participants (N=11) 

interacted with a robot using a synthesized machine-like or 

mechanical-like female voice. The used voice was in MARY 

called 'cmu-slt-hsmm'. The second group had a robot filter to 

create a more machine-like or mechanical voice. 

3.3 Experiment Setup 

The conducted experiment was combined with research about 

proxemics in human-robot interaction, or more specific the 

relationship between a robot's voice and proxemics. The 

experiment was divided in three parts. 

In the first part the robot tells its name and asks the participant 

to come closer. This part was needed for the other experiment 

and is also a good introduction of the robot. The participant 

walks towards the robot and the robot then tells the participant 

to do some simple tasks. The participant is asked to take a seat

at the table. The robot drives to the other side of the table, 

making sure the participant is sitting opposite of the robot. 

For the second part of the experiment, the participant 

performs six simple assignments. On the table are six cards 

with letters forming the word ‘thanks’ and also six (empty) 

numbered boxes. Three of the cards are faced up and show the 

letter, the others show the backside which is colored (red, 

green and blue). The six assignments are small assignments 

such as ‘move the letter N to box 2’, ‘swap the letter N with 

the letter H’ or ‘please turn over all colored squares’. The start 

of each of the assignments was triggered by hand using a 

Wizard of Oz technique. After some assignments the 

participant tells the robot how many boxes are left empty to 

enforce interaction with the robot. After moving all cards the 

robot asks the participant to flip all colored cards and read the 

word in the boxes. The robot then thanks the participant and 

drives to the table on the left-hand side of the participant. 

The third part of the experiment is very small. When the robot 

arrives at the table it turns around and asks the participant to 

come closer. This part is also needed for the other experiment. 

The robot tells the participant there is a questionnaire on the 

table next to her and asks the participant to fill it in. The 

experiment has ended. 

The figure below shows a picture taken from the experiment 

setup. The table with the six squares is in front of a platform 

on which the robot drives around. This was necessary because 

otherwise the robot was too small and the participant would 

not have been able to see the robot’s eyes. 

The moving assignments are similar to the ones used by 

Staudte et al. [14]. However this experiment is not based on 

utterance or gesture, but instead focuses on the two different 

voices. 

Figure 2: the experiment setup. 

3.4 Questionnaire & video footage 

A three-page questionnaire was used to determine if a person 

was engaged or interested in the robot or task. The 

questionnaire is based on measurement instruments for 

measuring anthropomorphism, animacy, likeability, perceived 

intelligence and perceived safety of robots provided by 

Bartneck et al. [2] to get understanding of how the robot was 

perceived by the participants. The questionnaire also contains 

questions on the attention allocation scale provided by Harms 

and Biocca [6] to find out if the participants were interested in 

the robot or the task. 

The questionnaire consists of five parts. The first part is about 

some general information. The second, third and fifth part 

contains 32 questions about the experiment on a 5-point 

Likert scale provided by Bartneck et al. [2]. The fourth part 

contains 9 questions which are rated on a scale from 1 to 7, of 

which some are provided by Harms and Biocca [6]. Because 

the conducted experiment is shared with Rens Hoegen not all 

questions will be used for providing answers in this paper. 

3.4.1 Most important questions 

The categories ‘rating of the participant’s’ have some 

important questions in order to answer the research questions: 

- artificial / lifelike & mechanical / Organic: 

questions in the categories anthropomorphism and 

animacy to determine how the robot's appearance 

was perceived. 

- unfriendly / friendly & unpleasant / pleasant: 

questions in the category likeability to rate the 

impression of the robot. 

- incompetent / competent & unintelligent / 

intelligent: questions in the category perceived 

intelligence. 

Another category contains questions to which the participant 

can disagree or agree. The most important are: 

- I was interested in the robot. 

- I was interested in the task. 

- I remained focused on the robot throughout our 

interaction. 

- I remained focused on the task throughout our 

interaction. 

The questionnaire ends with a manipulation check to 

determine if the difference in voices was noticed. The 

experiment was also recorded by two different cameras (one 

full HD camcorder and one fisheye camera) in order to 

support the questionnaire. Some small screenshots taken from 

the video footage can be found in section 4.2. The questions 

of the questionnaire can be found in Appendix A. 

4. RESULTS 

Most of the 41 questions from the questionnaire were used to 

analyze the relationship between voices and the level of 

interest. Some questions were not relevant for this research 

because it was a combined experiment. At first it is important 

to determine the reliability of the questions, after that the 

questions will be used to answer the research questions. 

Finally some screenshots of the video footage is shown to 

highlight some details. 

Figure 3: a schematic representation of the experiment.

4.1 Questionnaire 

The categories anthropomorphism, animacy, likeability, 

perceived intelligence and perceived safety suggested by 

Bartneck et al. [2] were all used in the questionnaire. 

4.1.1 Reliability 

The Cronbach Alpha value of each of the categories is listed 

in the third column of the table below. The second column 

shows which questions were used in that particularly 

category. The matching questions can be found in Appendix 

A. 

Table 1: Cronbach Alpha values for each category before 

removing questions. 

Category Questions (#) Cronbach Alpha 

Anthropomorphism 1, 5, 9, 13, 17 0.62 

Animacy 2, 6, 10, 13, 14, 

18 

0.62 

Likeability 3, 7, 11, 15, 19 0.81 

Perc. intelligence 4, 8, 12, 16, 20 0.61 

Perc. safety 24, 25, 26 0.67 

Because most Cronbach Alpha values were not very reliable 

some questions have been deleted. To achieve a Cronbach 

Alpha value of at least 0.70 in each category the following 

questions have been deleted: 

- moving rigidly / moving elegantly & unconscious / 

conscious in the category anthropomorphism. 

- apathetic / responsive, stagnant / lively & inert / 

interactive in the category animacy. 

- foolish / sensible in the category perceived 

intelligence. 

- quiescent / surprised in the category perceived 

safety. 

It is important to state that some participants had trouble with 

the meaning of apathetic and quiescent, all participants were 

Dutch. 

This results in the following Cronbach Alpha values: 

Table 2: Cronbach Alpha values for each category after 

removing questions. 

Category Questions (#) Cronbach Alpha 

Anthropomorphism 1, 5, 13 0.78 

Animacy 2, 10, 13 0.72 

Likeability 3, 7, 11, 15, 19 0.81 

Perc. intelligence 4, 8, 12, 16 0.72 

Perc. safety 24, 25 0.85 

4.1.2 Analysis 

In the tables below the results of the Independent Samples T- 

tests is shown. In table 3 the results of the test are shown for 

Bartneck’s categories. The results of the questions used in the 

manipulation check are shown in table 4. 

The other questions, including the questions taken from 

Harms and Biocca are evaluated separately in tables 5 and 6. 

Table 3: Independent Samples T-tests results for 

Bartneck’s categories with both conditions. 

Category Voice M SD t(19) p 

Anthropomorphism 

Animacy 

Likeability 

Perceived 

intelligence 

Perceived 

safety 

Human-like 2.47 0.48 

Machine-like 1.97 0.64 









2.00 0.060 

0.70 0.491 

2.42 0.026 

1.15 0.266 

-0.37 0.719 

Analyzing and comparing the two tables above shows that the 

categories animacy, perceived intelligence and perceived 

safety have no significant difference between the human-like 

voice and the machine-like voice. The category likeability on 

the contrary does show a significant difference between the 

human-like voice and the machine-like voice. The category 

anthropomorphism was approaching significance. 

Table 4: Independent Samples T-tests results for the 

manipulation check with both conditions. 

Question Voice M SD T(19) p 

Machinelike 

/ Humanlike 

Unpleasant 

/ Pleasant 

Disengaging 

/ Engaging 

Unclear 

/ Clear 









1.02 0.319 

0.54 0.595 

-0.68 0.504 

2.39 0.027 

The questions machinelike / humanlike, unpleasant / pleasant 

& disengaging / engaging in the manipulation check about the 

voice only showed minor differences, mostly in favor of the 

human-like model. The most important significant difference 

can be found in the question unclear / clear. It showed that the 

human-like voice was more clear than the machine-like voice 

and that a difference in voices was noticed. This can also be 

seen in the video footage, discussed in section 4.2 below. 

Some 7-point scale questions are excluded in this research, the 

others are listed below in tables 5 and 6. For each of the 

questions the Independent Samples T-test is performed and 

listed below. 


remaining questions with the human-like voice. 

Question 

M SD t(19) p 

I feel that the robot is interesting to look at. 

4.80 1.03 1.65 0.115

I was interested in the robot. 

5.70 0.48 1.13 0.273 

I was interested in the task. 

4.70 1.57 0.48 0.636 

I was easily distracted from the robot when other things were 

going on. (Recoded) 

5.40 1.17 1.11 0.281 

I remained focused on the robot throughout our interaction. 

4.90 1.52 -0.95 0.352 

I remained focused on the task throughout our interaction. 

5.10 1.20 -1.66 0.112 

Understanding the robot was difficult. (Recoded *) 

5.50 1.18 1.86 0.078 

voices. These pictures will be used in section 6 to support the 

conclusion. 

Figure 3: the participant is fixating on the robot after 

completing an assignment. 


remaining questions with the machine-like voice. 

Question 

M SD t(19) p 

I feel that the robot is interesting to look at. 

3.82 1.601 1.65 0.115 

I was interested in the robot. 

5.27 1.104 1.13 0.273 

I was interested in the task. 

4.36 1.629 0.48 0.636 

I was easily distracted from the robot when other things were 

going on. (Recoded) 

4.73 1.555 1.11 0.281 

I remained focused on the robot throughout our interaction. 

5.45 1.128 -0.95 0.352 

I remained focused on the task throughout our interaction. 

5.82 0.751 -1.66 0.112 

Understanding the robot was difficult. (Recoded *) 

4.36 1.567 1.86 0.078 

Figure 4: her facial expressions indicate that she has 

difficulties with understanding the robot. 

The above tables show that the differences between the two 

voices in terms of interest is not very significant. Thought it is 

interesting to know that there is a slight difference in focusing 

on either the task or the robot. 

* Note that some questions are recoded to make sure that all 

questions are rated the same way, meaning that 1 = negative, 

4 = neutral and 7 = positive. In the question ‘Understanding 

the robot was difficult.’ 1 was positive (not difficult) and 7 

was negative (difficult), therefore the question is recoded to 

match its rating with the other questions. 

4.2 Video footage 

In the previous section it became clear that the human-like 

voice was more likeable and that understanding the robot was 

slightly more difficult with the machine-like voice. The 

machine-like voice was rated as more unclear compared to the 

human-like voice. The video footage showed the exact same 

results. The pictures below show some information about the 

Figure 5: the participant is staring at the background, 

thinking about what the robot just said. 

5. DISCUSSION 

The difference between the two used voices turned out to be 

not very significant. The most important differences were 

found in Bartneck's category likeability and the question 

unclear / clear in the manipulation check. The human-like 

voice scored higher on the likeability scale and was found 

more clear than the machine-like voice. The questions in the 

category anthropomorphism were only approaching 

significance and showed that the human-like voice was 

perceived as slightly more anthropomorphic. There was also a 

minor difference in understanding the robot. The machine-like 

voice was harder to understand than the human-like voice, 

which relates to the difference found in the unclear / clear 

question.

The relationship between voice and interest is not significant. 

In the introduction the question 'If a robot is perceived as 

artificial and uses a mechanical voice instead of the usual 

human-like voice, does this influence the way humans are 

engaged to perform a certain task' came to mind. The results 

show that the influence of the voices is minimal. The robot 

with the human-like voice was perceived only a little more 

interesting. 

The results in table 5 and 6 appear to show a contradiction. 

The human-model scored slightly better on the questions 

about interest. However, the questions about focus show a 

minor difference in favor of the machine-like voice. 

6. CONCLUSION 

As mentioned above, some results appear to be contradicting. 

Though these results are not as significant as hoped, the 

difference can still be explained. Because the machine-like 

voice was rated less clear than the human-like voice, some 

participants had difficulties with understanding the robot. 

Section 4.2 shows three pictures taken from the video footage. 

Figure 4 shows a participant with facial expressions indicating 

it takes some effort to understand the robot. Figure 5 shows a 

participant thinking about what the robot said. It seems that 

the participants with the machine-like voice focused more on 

the robot because of the unclear voice. 

With the above results, the research questions mentioned in 

section 1.2 can be answered: 

This research showed that a robot's voice does not 

significantly influence the way participants are engaged to 

perform a certain task. Nor were the participants significantly 

more interested in the robot or task. 

The human-like voice in general scored overall better than the 

machine-like voice. This result is similar to the results found 

by other researchers mentioned in the introduction. 

7. FUTURE WORK 

In order to completely understand the relationship between 

voice and engagement, more research needs to be conducted. 

To improve this research better utilities are necessary. The 

used Magabot did not have a body (or body movement) and 

lacked facial expressions. To improve this experiment some 

aspects of the robot such as embodiment and perceived gender 

(more than just the voice of the robot) should be implemented. 

The robot was not quite socially interactive. 

The video footage could have been of greater importance to 

this research. Due to time issues, it was only possible to use 

the video footage as support to the questionnaire. The analysis 

of the video footage might reveal more interesting facts in a 

larger study. 

Some participants asked how the robot knew they were 

finished with a certain task. Parts of the experiment were 

conducted using the Wizard of Oz technique, which might 

have influenced the way the participant perceived the robot. In 

the future it is necessary to create a robot which responds 

autonomous based on the actions of the participant. 

Future work on this research might use a larger group of 

participants with equally mixed gender and a less technical 

background. The experiment was also conducted in a taskbased 

environment, experimenting in a more real-life situation 

would be more appropriate. 

8. ACKNOWLEDGEMENTS 

I would like to thank Betsy van Dijk and Manja Lohse for 

providing guidance and solving problems during the research. 

And I would also like to thank the University of Twente for 

providing all necessary utilities to conduct the experiments. 

My thanks also go to Gilberto Sepúlveda Bradford and Rens 

Hoegen for helping with the robot and the experiments. 

9. REFERENCES 

[1] C. Bartneck and J. Forlizzi. A Design-Centred 

Framework for Social Human-Robot Interaction. In 

Proceedings of the Ro-Man2004, Kurashiki pp. 591-594. 

2004. ACM. 

DOI: http://dx.doi.org/10.1109/ROMAN.2004.1374827 

[2] C. Bartneck, D. Kulić, E. Croft and S. Zoghbi. 

Measurement Instruments for the Anthropomorphism, 

Animacy, Likeability, Perceived Intelligence, and 

Perceived Safety of Robots. In International Journal of 

Social Robotics, Vol 1(1), 71-81. 2009. 

DOI=http://dx.doi.org/10.1007/s12369-008-0001-3 

[3] C.R. Crowelly, M. Villanoy, M. Scheutz and P. 

Schermerhorn. Gendered voice and robot entities: 

Perceptions and reactions of male and female subjects. In 

Intelligent Robots and Systems, IROS 2009, IEEE/RSJ 

International Conference, 3735-3741. 2009. 

DOI=http://dx.doi.org/10.1109/IROS.2009.5354204 

[4] J. Goetz, S. Kiesler, and A. Powers. Matching robot 

appearance and behavior to tasks to improve humanrobot 

cooperation. In Proceedings of ROMAN 2003, the 

12th IEEE International Workshop on Robot and Human 

Interactive Communication, 55-60, 2003. ACM. 

DOI= http://dx.doi.org/10.1109/ROMAN.2003.1251796 

[5] L. Gong and C. Nass. When a talking-face computer 

agent is half-human and half-humanoid: human identity 

and consistency preference. In Journal of Human 

Communication Research, Vol 33(2), 163–193. 2007. 

DOI=http://dx.doi.org/10.1111/j.1468-958.2007.00295.x 

[6] C. Harms and F. A.Biocca. Internal consistency and 

reliability of the networked minds social presence 

measure. In M. Alcaniz & B. Rey (Eds.), Seventh Annual 

International Workshop. 2004. ACM. 

DOI=http://cogprints.org/7026/ 

[7] K. M. Lee, W. Peng, S. Jin and C. Yan. Can robots 

manifest personality An empirical test of personality 

recognition, social responses, and social presence in 

human-robot interaction. In Journal of Communication, 

56, 754-772. 2006. 

DOI=http://dx.doi.org/10.1111/j.1460-466.2006.00318.x 

[8] J. A. LePine and L. Van Dyne. Voice and cooperative 

behavior as contrasting forms of contextual performance: 

Evidence of differential relationships with Big Five 

personality characteristics and cognitive ability. In 

Journal of Applied Psychology, Vol 86(2), 326-336. 

2001. 

DOI=http://psycnet.apa.org/doi/10.1037/0021- 

9010.86.2.326 

[9] K. MacDorman and H. Ishiguro. The uncanny advantage 

of using androids in cognitive and social science 

research. Interaction Studies, Vol 7(3), 297–337. 2006. 

DOI=http://dx.doi.org/10.1075/is.7.3.03mac

[10] B. Mutlu, J. Forlizzi and J. Hodgins. A Storytelling 

Robot: Modeling and Evaluation of Human-like Gaze 

Behavior. In Proceedings of HUMANOIDS’06, 2006. 

ACM. 

DOI= http://dx.doi.org/10.1109/ICHR.2006.321322 

[11] Y. Okuno, T. Kanda, M. Imai, H. Ishiguro, and N. 

Hagita. Providing route directions: design of robot’s 

utterance, gesture, and timing. In Proceedings of the 4th 

ACM/IEEE international conference on Human robot 

interaction, HRI’09, 53-60, 2009. ACM. 

DOI=http://doi.acm.org/10.1145/1514095.1514108 

[12] C. Peters, G. Castellano and S. de Freitas. An exploration 

of user engagement in HCI. In Proceedings of the 

affective-aware virtual agents and social robots 

(AFFINE) workshop, international conference on 

multimodal interfaces, ICMI’09, 2009. ACM. 


[13] M. Siegel, C. Breazeal and M. I. Norton. Persuasive 

Robotics: The influence of robot gender on human 

behavior. In Intelligent Robots and Systems, IROS 

(2009). 2563-2568. 2009. ACM. 

DOI=http://dx.doi.org/10.1109/IROS.2009.5354116 

[14] M. Staudte and M. Crocker. Visual attention in spoken 

human-robot interaction. In Proceedings of the 4th 

ACM/IEEE international conference on Human robot 

interaction, HRI’09, 77–84, 2009. ACM. 


[15] A. Steinfeld, T. Fong, D. Kaber, M. Lewis, J. Scholtz, A. 

Schultz and M. Goodrich. Common metrics for humanrobot 

interaction. In Proceedings of the 1st ACM/IEEE 

International Conference on HumanRobot Interactions, 

HRI'06, 2006. ACM. 

DOI= http://dx.doi.org/10.1145/1121241.1121249 

[16] M. L. Walters, K. Dautenhahn, R. te Boekhorst, K. L. 

Koay, C. Kaouri, S. Woods, C. Nehaniv, D. Lee, and I. 

Werry. The influence of subjects’ personality traits on 

personal spatial zones in a human-robot interaction 

experiment. In Proceedings of COG SCI 2005: Toward 

Social Mechanisms of Android Science Workshop, 29-37, 

2005. ACM. 

DOI= http://dx.doi.org/10.1109/ROMAN.2005.1513803 

[17] M. L. Walters, D. S. Syrdal, K. Dautenhahn, R. te 

Boekhorst and K. L. Koay. Avoiding the uncanny valley: 

robot appearance, personality and consistency of 

behavior in an attention-seeking home scenario for a 

robot companion. Autonomous Robots, 159-178. 2008. 

DOI=http://dx.doi.org/10.1007/s10514-007-9058-3 

[18] M. L. Walters, D. S. Syrdal, K. L. Koay, K. Dautenhahn, 

R. te Boekhorst. Human approach distances to a 

mechanical-looking robot with different robot voice 

styles. In the 17th IEEE International Symposium on 

Robot and Human Interactive Communication, RO- 

MAN (2008), 707-712. 2008. 

DOI=http://dx.doi.org/10.1109/ROMAN.2008.4600750

APPENDIX 

A. RESULTS QUESTIONNAIRE 

Table A1: first part of the questionnaire containing questions rated on a scale from 1 to 5. 

Human-like voice 

Machine-like voice 

# Questions Mean Std. Deviation Mean Std. Deviation 

Rate the impression of the robot on a scale from 1 to 5. 

1 Fake / Natural 3.20 0.632 2.45 0.820 

2 Dead / Alive 2.90 1.101 2.55 1.036 

3 Dislike / Like 4.00 0.667 3.27 0.647 

4 Incompetent / Competent 3.50 0.850 3.55 0.934 

5 Machinelike / Humanlike 1.90 0.738 1.64 0.674 

6 Stagnant / Lively 3.10 1.370 2.45 0.820 

7 Unfriendly / Friendly 4.30 0.675 3.73 0.467 

8 Ignorant / Knowledgeable 3.40 0.699 3.09 0.701 

9 Unconscious / Conscious 3.10 0.876 2.82 0.874 

10 Mechanical / Organic 1.50 0.707 1.73 0.647 

11 Unkind / Kind 4.10 0.738 3.55 0.522 

12 Irresponsible / Responsible 3.40 0.516 3.00 0.632 

13 Artificial / Lifelike 2.30 0.483 1.82 0.751 

14 Inert / Interactive 3.60 0.699 3.09 0.831 

15 Unpleasant / Pleasant 3.70 0.675 3.45 0.688 

16 Unintelligent / Intelligent 3.60 0.516 3.18 0.874 

17 Moving rigidly / Moving elegantly 2.80 0.919 2.27 1.009 

18 Apathetic / Responsive 3.50 0.850 3.36 0.674 

19 Awful / Nice 3.90 0.738 3.55 0.688 

20 Foolish / Sensible 3.00 0.471 3.55 0.688 

21 Quiet / Loud 2.60 0.699 2.64 0.674 

22 Unhelpful / Helpful 3.50 0.527 3.45 0.820 

23 Intimidating / Inviting 3.60 0.699 3.55 0.820 

Rate your emotional state on a scale from 1 to 5. 

24 Anxious / Relaxed 3.70 0.949 3.91 0.539 

25 Agitated / Calm 3.80 0.789 3.82 0.751 

26 Quiescent / Surprised 2.50 0.850 3.00 0.894 

27 Unsafe / Safe 4.00 1.054 4.27 0.647 

28 Pressured / At ease 3.70 0.823 3.45 0.688

Table A2: second part of the questionnaire containing questions rated on a scale from 1 to 7. 




Give your opinion on the following statements. (Scale from 1 to 7, strongly disagree to strongly agree.) 

29 I feel that the robot is interesting to look at. 4.80 1.033 3.82 1.601 

30 I was interested in the robot. 5.70 0.483 5.27 1.104 

31 I was interested in the task. 4.70 1.567 4.36 1.629 

32 I was easily distracted from the robot when other things 

were going on. 

33 I remained focused on the robot throughout our 

interaction. 

34 I remained focused on the task throughout our 

interaction. 

5.40 1.174 4.73 1.555 

4.90 1.524 5.45 1.128 

5.10 1.197 5.82 0.751 

35 Understanding the robot was difficult. 5.50 1.179 4.36 1.567 

36 Throughout our interaction I became more familiar with 

the robot. 

5.20 0.919 4.45 1.508 

37 I felt uncomfortable when I was close to the robot. 5.30 0.949 5.55 1.695 

Table A3: third part of the questionnaire containing questions rated on a scale from 1 to 5, used as the manipulation check. 




Rate your impression of the voice of the robot on the following scales. 

38 Machinelike / Humanlike 2.00 1.054 1.64 0.505 

39 Unpleasant / Pleasant 3.20 0.919 3.00 0.775 

40 Disengaging / Engaging 2.90 0.568 3.09 0.701 

41 Unclear / Clear 3.20 0.919 2.36 0.674

s Voice to Make Human-Robot Interaction More Engaging

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