2011 RO-MAN
20th IEEE International Symposium on Robot andHuman Interactive Communication,
July 31 - August 3, 2011, Atlanta, GA, USA

Blinking Light Patterns as Artificial Subtle Expressions in
Human-Robot Speech Interaction
Kazuki Kobayashi
Graduate School of Science andTechnology
Shinshu University
4-17-1 Wakasato, Nagano
380–8553, Japan

Kotaro Funakoshi
Honda Research Institute Japan
Co., Ltd.
8-1 Honcho, Wako, Saitama
351-00188, Japan
funakoshi@jp.honda-ri.com

Seiji Yamada
National Institute of Informatics /SOKENDAI / Tokyo Institute ofTechnology
2-1-2 Hitotsubashi, Chiyoda, Tokyo
101–8430, Japan

kby@shinshu-u.ac.jp

seiji@nii.ac.jp

Mikio Nakano
Honda Research Institute Japan
Co., Ltd.
8-1 Honcho, Wako, Saitama
351–0188, Japan

Takanori Komatsu
International Young Researcher
Empowerment Center
Shinshu University
3-15-1 Tokida, Ueda, 386–8567, Japan

Yasunori Saito
Faculty of Engineering
Shinshu University
4-17-1 Wakasato, Nagano
380–8553, Japannakano@jp.honda-ri.com

tkomat@shinshu-u.ac.jp

saitoh@cs.shinshu-u.ac.jp

to recognize subtle facial expressions because people do notalways make clear facial expressions in real situations.
On the basis of such subtle changes in these social signals,
some researchers have tried to implement humanoid robots
and life-like agents for smooth communication with subtle
expressions. Bartneck & Reichenbach [9] investigated theeffect of the geometrical intensity of a synthetic facial
expression for developing synthetic characters. Sugiyama etal. [10] created a humanoid robot that can slightly change its
behaviors on the basis of recognition of its situation. Kipp
& Gebhard [11] developed a dexterous avatar agent that canslightly change its facial expression in accordance with theuser’s gaze direction. However, implementing subtle expressions requires many joints and complicated control systems
for robots and life-like agents, making implementation very
expensive.
We found that simple expressions from artificial agents,
like a blinking LED or beeping sounds, played a similar role
to subtle expressions made by humans. For example, smooth
turn taking was achieved in human-robot speech interaction
by a robot conveying an internal state of recognizing theuser’s utterances with a small blinking LED [12], [13]. Anagent communicated its attitude to users by making inflected
beeping sounds [14]. A robot communicated confidence
levels of its advice to users by adding inflected beeping
sounds [15], [16]. On the basis of the results of thesestudies, we proposed “artificial subtle expressions (ASEs)”
as intuitive notification methodology for describing artificial
agents’ internal states to users.
The advantages of ASEs are low cost implementation andintuitive notification without previous knowledge. Moreover,
ASEs made by a blinking LED gave users better impressionsof dialogue with a robot and the robot itself [13], [12].
However, how expression patterns affect user impressions

Abstract— Users’ impressions of blinking light expressions
used as artificial subtle expressions have been investigated. In
a preliminary experiment, thirteen blinking patterns were used
for investigating participants’ impressions of their agreeableness. The highest and lowest valued blinking patterns wereidentified and used for a speech interaction experiment. In thisexperiment, 52 participants tried to reserve hotel rooms with aspoken dialogue system coupled with an interface robot usinga blinking light expression. A sine wave, a random wave, arectangular wave, and a no-blinking condition were used as
artificial subtle expressions to express a robot’s internal stateof “processing” or “recognizing”. The results of a questionnaire
showed the conditions did not significantly differ in terms ofagreeableness, but the sine wave and the rectangular wave
were evaluated as “more useful” than the no-blinking condition.
Results of factor analyses suggested that the rectangular wave
provides a comfortable impression of the dialogue.

I. I NTRODUCTION
The research area of human robot/agent interaction deals
with smooth communications between inventions and humans. Inventions that can communicate with people can improve our daily life and business processes. Some researchers
have tried to develop robots and agents that can behave ina human-like manner [1], [2]. Specifically, some researchers
have focused on subtle changes in inventions’ behavior that issimilar to changes in humans’ behavior. People usually communicate with each other verbally, but nonverbal information,such as facial expressions, gaze directions, and gestures, also
plays an important role [3]. People can easily understandothers’ internal states from such nonverbal information [4].
Even subtle changes in nonverbal information influence
human communications. Such expressions are called subtle
expressions [5]. Ward [6] reported that the subtle inflectionsof pitch information in speech reflect one’s emotional stateseven when contradicted by the literal meanings of the words
spoken. Cowell & Ayesh [7] and Song et al. [8] have tried
978-1-4577-1572-3/11/$26.00 ©2011 IEEE181

Fig. 1.

LED blinking patterns

is not clear. In this study, we focused on ASEs made by ablinking LED and investigated the relationship between LED
blinking patterns and user impressions.

 II. E XPERIMENT OF B LINKING PATTERN E VALUATION
  

We conducted an evaluation experiment for users’ impressions of LED blinking patterns without speech interaction.



A. Method
We investigated the relationship between user impressions
and blinking patterns by using a small LED. The patterns weused were thirteen waves: three intermittent chaos waves,
three random waves, three rectangular waves, three sine
waves, and always-on. Figure 1 shows twelve of the patterns
used in our experiment (always-on is left out). The verticalaxes are proportional to the voltages input to the LED. The
LED was charged from 0V to 5V with a 10 KΩ registerconnected in series. Intermittent chaos waves and random
waves have parameters of update time. For example, an intermittent chaos wave with a 0.033-sec update time is describedas “Chaos (0.033 sec)” in the figure. Rectangular waves
and sine waves have parameters of frequency. For example,
a rectangular wave with 0.5-Hz frequency is described as“Rectangular (0.5 Hz)” in the figure.
Figure 2 shows the experimental environment of the investigation. A red LED 4 mm in diameter was fixed in the centerof a black board. Participants’ visual fields were restrictedwith the black boards so that they could concentrate on the
LED blinking. They were requested to give their impressions

Fig. 2.

Experimental environmentof LED patterns by pushing an evaluation button. Luminanceof the room was 187 LUX near the LED.
Participants selected a number from 1 to 7 on the basisof seven-level Likert items after a LED pattern was shown.
Number 1 meant “bad feeling”, and 7 meant “good feeling”.
Blinking time was 4 sec in each condition. Before an LED
pattern appeared, a short notification sound was played. LED
patterns were shown in a random order, and each pattern wasshown three times from the random set; 39 LED blinking
patterns were provided for a participant. The participants
were 16 males, mean age 23.5 (S.D. = 1.7).
182

TABLE II
R ATED ITEMS OF IMPRESSION OF LED AND RESERVATION SYSTEM
item
good feeling–bad feeling(for the LED)useful–useless(for the reservation system)
hope to use–hope not to use(for the reservation system)
the time the robot took to began to reply tothemsine (0.5 Hz)
Mean
S.D

rectangle (15 Hz)
Mean
S.D.

random (0.033 sec)
Mean
S.D

no-blinking
Mean S.D.

4.58

0.79

4.31

1.18

4.23

0.60

—

—

4.69

1.11

4.77

1.54

3.62

1.45

3.00

1.73

3.69

1.49

4.46

1.85

3.92

1.71

3.23

1.92

3.23

1.75

2.92

1.31

2.77

1.63

4.35

2.83

TABLE IR ATED VALUES OF LED BLINKING PATTERNSBlinking patterns
Sine (0.33Hz)
Sine (0.5Hz)
Sine (1Hz)
Rectangle (0.33Hz)
Rectangle (0.5Hz)
Rectangle (1Hz)
Chaos (0.033sec)
Chaos (0.1sec)
Chaos (0.2sec)
Random (0.033sec)
Random (0.1sec)
Random (0.2sec)
Always-On

Mean
5.04
5.65
4.73
4.17
4.63
4.40
2.15
3.00
3.23
1.88
1.96
2.63
4.17

S.D
1.35
0.77
0.98
0.78
0.91
1.17
0.95
0.61
0.77
1.42
0.69
0.88
1.46



Fig. 3.

Experimental environment and robot with embedded LED

LED blinking was able to prevent utterance collisions [13],[12].
The blinking patterns we used in this experiment were arandom wave (0.033 sec), a sin wave (0.5 Hz), a rectangularwave (15 Hz), and no-blinking. The random wave (0.033
sec) had the lowest value and the sine wave (0.5 Hz) had thehighest value in the experiment of blinking pattern evaluation
as described above. The pattern of the rectangular wave
(15 Hz) was used in our previous works [13], [12], [17].
The blinking time was a participant’s utterance time plus
4 seconds. The robot began to utter 3.6 seconds after it
stopped an audio signal from a participant because there wasa processing delay for 0.4 seconds.
Participants were requested to sit on a chair and to reserve
hotel rooms by talking with the robot placed in front of them.
They put on a head phone so that the spoken dialogue system
could easily distinguish their voices from the robot’s voice
and performed five hotel reservation tasks. In each task, they
tried to reserve one to three rooms. Reservation procedures
such as reserving multiple rooms at a time or reserving aroom a few different times were not provided. After the five
trials, they answered a questionnaire of their impressions of
the LED blinking and the reservation system, the dialogue,
and the robot. Participants were 26 males and 26 females.
Their mean age was 30.1 (S.D.=9.4), and ages ranged from19 to 50 years old. The luminance of the room was 475
LUX, and the 1 KΩ register was connected to the LED inseries to adjust the luminance of the LED.

B. Experimental Results
Table I shows the results of LED pattern evaluations. The
higher the value, the better the feeling. The sine wave (0.5
Hz) had the highest value, and the random wave (0.033 sec)
had the lowest value.
III. E XPERIMENT OF HUMAN - ROBOT SPEECH
INTERACTION

On the basis of results of the Blinking pattern evaluations,
we investigated the effects on the user impressions in humanrobot speech interaction.
A. Method
We conducted a speech interaction experiment between arobot with a small LED and the participants. Participants
were requested to reserve hotel rooms by talking with therobot. The spoken dialogue system and the robot (Wow Wee
RS-Media) were the same as those in our previous work [17].
The purpose of the experiment was to investigate how LED
blinking patterns affect the user impressions of the dialogue
and the robot.
A red LED 4 mm in diameter was fixed in the centerof the robot’s chest, as shown in Fig. 3. When the spoken
dialogue system detects an audio signal, the LED on the
robot begins to blink. The blinking stopped when the robot
began to answer verbally. The role of the LED blinking
was to show participants that the robot detects their voices
and processes. This prevents utterance collisions between aparticipant and the robot. Our previous work showed that the

B. Experimental results1) Impressions of the LED blinking and the reservation
system: Participants evaluated items to be “good feeling–bad
feeling” for the LED, “useful–useless” for the reservation
183

TABLE III
R ATED ADJECTIVE PAIRS FOR IMPRESSION OF DIALOGUE AND FACTOR LOADING MATRIX OF FACTOR ANALYSIS ( PROMAX ROTATION , MAXIMUM
LIKELIHOOD METHOD £Adjective pairs
Positive
Negative
exciting
dull
interesting
boringpleasant
unpleasantcomprehensible
incomprehensible
calmagitated
dislikablelikablelivelylifelessgoodpoorleisurely
hurriedsoothingannoying
politeimpoliteeasy
uneasyroughsmoothwarm
cold
informalformalrelaxedtense
casualseriousSine (0.5 Hz)
Mean
S.D
4.15
1.63
5.54
0.78
4.15
1.21
4.15
1.41
3.69
1.18
3.92
0.86
3.15
0.80
3.69
0.85
4.77
1.30
3.69
1.49
4.15
1.14
3.38
1.12
3.31
1.25
3.00
1.22
3.15
1.14
3.31
0.63
3.38
0.96

Rectangle (15Hz)
Mean
S.D
5.15
1.57
5.69
0.95
4.77
0.93
4.69
1.44
4.31
1.49
4.62
1.39
3.38
1.12
4.31
1.38
4.62
1.04
4.85
0.99
5.31
0.85
3.85
1.46
4.15
1.52
4.23
1.24
3.92
1.66
3.46
1.27
4.00
1.35

Random (0.033 sec)
Mean
S.D
4.46
1.76
5.31
1.44
4.15
1.07
3.85
1.63
4.08
1.12
4.00
1.29
3.23
0.73
3.69
1.03
5.31
0.95
3.62
1.19
4.23
0.93
3.08
0.95
2.77
1.42
3.54
1.39
3.54
1.39
3.23
0.73
3.31
0.85

No-blinking
Mean
S.D
3.92
1.61
4.77
1.59
3.92
1.19
4.00
1.87
3.69
1.49
3.54
1.90
3.15
1.34
3.69
1.70
4.15
1.46
3.23
1.42
4.38
1.19
2.92
1.66
2.69
1.65
3.23
1.36
2.77
1.59
3.69
1.65
3.69
1.44

1
0.95
0.85
0.60
0.54
-0.13
0.32
-0.05
0.18
-0.16
0.10
0.07
-0.05
-0.04
-0.04
0.05
0.01
-0.09

2
-0.21
-0.08
0.28
-0.01
0.95
0.67
0.56
0.55
0.49
0.20
-0.21
0.16
0.25
-0.01
0.27
0.04
-0.19

Factors
3
-0.02
-0.05
0.02
0.28
0.00
-0.09
0.38
0.11
0.27
0.87
0.62
0.61
0.57
0.04
0.16
-0.17
0.32

4
0.07
-0.01
-0.03
-0.18
-0.01
0.08
-0.10
0.02
0.03
-0.05
0.29
0.09
-0.08
1.04
0.46
-0.06
-0.06

5
-0.03
0.03
-0.16
0.08
-0.07
-0.01
-0.02
0.23
-0.09
-0.21
0.05
0.13
0.13
-0.11
0.19
0.88
0.52

TABLE IVC ORRELATION BETWEEN FACTORS OF IMPRESSION OF THE DIALOGUE

system, and hope to use–hope not to use ¡for the system.
They also answered the time the robot took to began to
reply to them. Table II shows the results of the questionnaire.
Participants evaluated items on a seven-level Likert scale (1:strong agreement with a negative adjective, 4: neutral, 7:strong agreement with a positive adjective). We performed
ANOVA for each item, which showed significant differencesin the “useful–useless” (F3,48 = 4.428, p < .01). The items
were compared by the Tukey HSD method multiple times,
which showed significant differences between the sine wave
and the no-blinking (p = .026) and between the rectangularwave and no-blinking (p = .018).

Factors
1
2
3
4
5

1
1.00
0.60
0.40
0.42
0.28

2
0.60
1.00
0.66
0.52
0.49

3
0.40
0.66
1.00
0.50
0.38

4
0.42
0.52
0.50
1.00
0.43

5
0.28
0.49
0.38
0.43
1.00

TABLE V
FACTOR SCORES OF THE IMPRESSION ON THE DIALOGUE

Factor
1. Excitement

2) Impressions of the dialogue: Table III shows the resultsof participants’ ratings for the dialogue. The adjective pairs
in the table are translated from Japanese words that we usedin the questionnaire. The ratings are based on a seven-level
Likert scale (1: strong agreement with a negative adjective,
4: neutral, 7: strong agreement with a positive adjective).
We performed factor analysis (promax rotation and maximum likelihood method) for the impression of the dialogue.
Five factors were extracted from evaluated items by a screeplot. In the factor analysis process, we excluded the lowvalue-factor loading item “light–dark”. We interpreted thefactors on the basis of our previous work [18]. The first factor
was named the excitement factor, the second the calmness
factor, the third the comfort factor, the fourth the relaxation
factor, and the fifth the warmness factor. Table IV shows thecorrelation between factors.
We calculated the factor scores by using a regression
method. Table V shows the factor scores and the results of
ANOVAs for the impression of the dialogue. The ANOVAs
found a significant difference in the third factor (comfort).
Multiple comparisons with the Tukey HSD method between
conditions found differences between rectangle and noblinking (p = .027) and between rectangle and random(p = .078). This suggests that the rectangular wave provides
a comfortable impression for users because the scores of therectangular wave are higher than those of the no-blinking

2. Calmness

3. Comfort

4. Warmness

5. Relaxation

Blinking pattern
sine
rectangle
randomno-blinking
sine
rectangle
randomno-blinking
sine
rectangle
randomno-blinking
sine
rectangle
randomno-blinking
sine
rectangle
randomno-blinking

Mean-0.0610.477-0.053-0.363-0.0930.346
0.027-0.279-0.1150.674-0.201-0.358-0.3620.532
0.016-0.186-0.0800.141-0.153
0.093

S.D
0.725
0.772
0.906
1.195
0.650
0.980
0.809
1.278
0.951
0.801
0.796
1.043
0.918
0.937
1.012
1.003
0.621
1.142
0.389
1.249

F3,48

p

1.880

0.146

0.979

0.410

3.372

0.026

2.072

0.116

0.297

0.827

condition and random wave.
3) Impression on the robot: Table VI shows the resultsof participants’ ratings for the robot. The adjective pairs in
the table are also translated from Japanese. The ratings are
based on a seven-level Likert scale as well as the ratings forthe dialogue.
We performed factor analysis (promax rotation and maximum likelihood method) for the impressions of the robot.
Four factors were extracted from evaluated items by a screeplot. In the process of the factor analysis, we excluded the184TABLE VI
R ATED ADJECTIVE PAIRS FOR IMPRESSIONS OF THE ROBOT AND FACTOR LOADING MATRIX OF FACTOR ANALYSIS ( PROMAX ROTATION , MAXIMUM
LIKELIHOOD METHOD £Adjective pairs
Positive
Negative
excitedcool
politeimpolitefriendly
unfriendly
innocent
wickedugly
prettyaccessible
inaccessible
sociableunsociable
aggressive
defensive
activeinactivenarrow-mindedbroad-mindedcarefulcarelesspatient
irritable
confident
unconfident
kindunkindmodestboastfulrespectful
disrespectful
discreet
indiscreet
impressive
unimpressive
seriousfrivolous

Sine (0.5 Hz)
Mean
S.D
3.46
1.20
4.15
0.69
4.08
1.38
4.31
0.63
4.31
1.03
3.54
0.88
3.38
0.96
4.31
0.63
3.92
0.76
3.92
0.76
5.08
0.76
4.69
0.85
4.69
0.95
4.08
0.95
4.31
0.85
4.31
0.85
4.15
0.80
5.23
1.09
5.00
1.00

Rectangle (15 Hz)
Mean
S.D
4.00
0.82
4.85
1.14
4.38
1.33
4.62
0.77
4.54
0.78
3.92
1.32
4.38
1.26
5.00
0.82
4.85
0.69
4.38
0.77
4.69
0.75
4.31
0.95
4.92
0.86
3.92
1.00
4.31
0.95
4.69
1.25
4.92
1.75
6.00
1.00
5.00
1.15

Random (0.033 sec)
Mean
S.D
3.54
1.05
4.00
0.71
3.77
1.09
4.15
0.80
4.08
0.76
3.31
0.95
4.00
1.22
3.92
0.76
4.31
0.85
4.31
0.95
4.85
0.99
4.92
1.50
4.85
1.46
4.15
0.69
4.38
0.65
4.54
0.97
4.62
0.96
4.62
1.26
4.31
0.85

TABLE VIIC ORRELATION BETWEEN FACTORS OF IMPRESSIONS OF THE ROBOTFactors
1
2
3
4

1
1.00
0.34
0.01
-0.19

2
0.34
1.00
-0.12
0.18

3
0.01
-0.12
1.00
0.08

1. Friendliness2. Credibility

3. Humility

4. Virility

Mean-0.1690.521-0.214-0.098-0.0540.216
0.074-0.220-0.205-0.227-0.094
0.509
0.007
0.266
-0.398
0.147

S.D
0.821
0.931
0.631
1.268
0.590
0.780
1.142
1.068
0.495
0.947
0.556
1.364
0.846
0.645
1.050
0.796

F3,47

p

1.635

0.194

0.505

0.681

1.879

0.146

1.469

0.235

Factors
2
3
-0.10
-0.12
-0.30
0.42
0.19
0.01
-0.01
0.09
-0.12
0.11
0.25
0.02
0.20
-0.09
0.37
-0.24
0.40
-0.20
0.15
0.19
0.07
0.38
0.80
0.22
0.66
0.20
0.36
0.12
0.29
0.79
-0.09
0.76
0.29
0.58
0.25
0.02
-0.03
0.11

4
0.10
0.27
0.01
0.00
-0.19
-0.25
-0.12
0.12
0.18
-0.45
0.32
-0.17
0.23
0.01
0.11
-0.20
0.14
0.59
0.48

The results of the speech interaction experiment suggestthat the rectangular wave provides comfortable impressions
in a dialogue. This is possibly due to the reliability of the
speech interaction. Participants might consider their words
were certainly conveyed to the robot. A 15-Hz rectangularwave is probably effective for users to have this impression
in a dialogue. This finding is useful to make users’ feel more
comfortable in speech interaction with an invention because
a simple blinking LED can be easily attached to it.
The characteristics of the 15-Hz rectangular wave werea fast switching blink, a widely changed luminance, anda constant frequency. These might provide people with afeeling that their words are being reliably conveyed because
in previous work [18] with rectangular LED blinking people
were also given a similar impression during hotel reservation
tasks.
The experimental results of the speech interaction alsosuggest that LED blinking patterns do not significantly affectthe impression of the robot. If the speech interaction in hotel
reservation tasks required more attention than the robot itself,
it is no wonder that the LED blinking had little effect on the
robot impression. A previous work with a last-and-first task
[12] also used a rectangular wave for LED blinking, andits experimental results showed the LED blinking provided
a sincere impression of the robot. The sincere impressionof the robot and the comfortable impression of the dialogue
seem to be related. This study used hotel reservation tasks
that were more complicated than last-and-first tasks. Sucha difference should make participants’ interests shift to the
dialogue from the robot itself because the dialogue is directly
connected to their purpose in the given tasks.
The experimental results of the blinking pattern evaluation
showed that the sine wave (0.5 Hz) had the highest value
and the random wave (0.033 sec) had the lowest value. In
contrast, the evaluation values of the sine wave and therandom wave in the experiment of the speech interaction

4
-0.19
0.18
0.08
1.00

TABLE VIII
Blinking pattern
sine
rectangle
randomno-blinking
sine
rectangle
randomno-blinking
sine
rectangle
randomno-blinking
sine
rectangle
randomno-blinking

1
0.79
0.78
0.73
0.68
0.67
0.61
0.50
0.49
0.48
0.48
-0.39
-0.07
-0.09
0.17
-0.28
0.36
0.07
0.21
-0.18

IV. D ISCUSSION

FACTOR SCORES OF THE IMPRESSION ON THE ROBOTFactorNo-blinking
Mean
S.D
3.85
1.21
4.85
1.52
4.00
1.29
4.15
0.90
4.08
0.86
3.62
1.56
3.54
1.27
3.85
1.34
3.62
1.19
4.15
0.99
4.85
1.21
4.62
1.12
4.69
1.11
4.31
0.95
5.00
1.00
4.85
1.63
4.92
1.04
5.38
1.04
5.38
1.45

low-value-factor loading item “responsible–irresponsible”.
We also interpreted the factors on the basis of our previous
work. The first factor was named the friendliness factor, thesecond the credibility factor, the third the humility factor, andthe fourth the virility factor. Table VII shows the correlation
between factors, none of which were high-value factors.
We also calculated the factor scores by using a regression
method. Table VIII shows the factor scores and the resultsof ANOVAs for the impressions of the robot. The ANOVAs
found no significant difference between blinking patterns.
This suggests that the LED blinking patterns do not significantly affect the impressions of the robot.
185were almost the same. These results were likely caused by
the difference in tasks. Hotel reservation tasks in the speechinteraction experiment might have required more attention
than observing the LED in the pattern evaluation experimentbecause the participants were required to reserve hotel rooms.
Therefore, they would not have been as conscious of theirimpressions of the LED and so gave them almost the sameratings. How tasks affect impressions needs to be analyzed.
In our previous works, we used the 15-Hz rectangularwave for LED blinking. The effects of blinking patterns
were investigated in this study, and the results suggested thatthe rectangular wave was effective for giving participants
better impressions of the dialogue. Such a wave form of
LED blinking will be effective in tasks requiring speechinteraction. However, effectiveness of the LED blinking will
be potentially affected by contexts because it is an abstract
expression and can be interpreted differently by users. The
meanings conveyed by the LED blinking in different contexts
need to be investigated.

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V. C ONCLUSION
In this paper, we described the investigations into users’
impression given by blinking light expressions used as artificial subtle expressions (ASEs). In the preliminary experimentof blinking pattern evaluation, thirteen blinking patterns wereused for investigating participants’ impressions of the agreeableness (good feeling-bad feeling) of the artificial subtle
expressions. The highest valued blinking pattern was the sine
wave (0.5 Hz), and the lowest valued one was the random
wave (0.033 sec). In the speech interaction experiment, 52
participants engaged in hotel room reservation tasks with aspoken dialogue system coupled with an interface robot usinga blinking light expression. A sine wave (0.5 Hz), random
wave (0.033 sec), rectangular wave (15 Hz), and no-blinking
condition were used as artificial subtle expressions to express
the robot’s internal states of “processing” or “recognizing”.
The results of a questionnaire showed that the blinking
patterns and the sine wave did not significantly differ in termsof agreeableness, and the rectangular wave was evaluated as
more useful than the no-blinking condition. The results ofthe factor analyses suggest that the rectangular wave gives
participants a comfortable impression of the dialogue.
We focused on ASEs made by a blinking LED andinvestigated the relationship between blinking patterns ofa LED and user impressions. The advantages of ASEs are
low cost implementation and intuitive notification without
previous knowledge. Our findings in this study are useful toimprove users’ feeling in speech interaction with an invention
because a simple blinking LED can be easily attached toit. We will address an investigation on conveying meaningsof the LED blinking in different contexts to resolve theremaining issues.
R EFERENCES[1] K. Kadowaki, K. Kobayashi, and Y. Kitamura, “Influence of social relationships on multiagent persuasion,” in Proc. of the 7th International
Conference on Autonomous Agents and Multiagent Systems, 2008, pp.
1221–1224.

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