Non-humanlike Spoken Dialogue: A Design Perspective
Kotaro Funakoshi
Mikio Nakano
Honda Research Institute Japan Co., Ltd. Honda Research Institute Japan Co., Ltd.
8-1 Honcho, Wako
8-1 Honcho, Wako
Saitama, Japan
Saitama, Japan
funakoshi@jp.honda-ri.com
nakano@jp.honda-ri.com
Seiji Yamada
Takanori Komatsu
Kazuki Kobayashi
National Institute of Informatics
Shinshu University
Shinshu University
2-1-2 Hitotsubashi, Chiyoda
3-15-1 Tokida, Ueda
4-17-1 Wakasato, Nagano
Tokyo, Japan
Nagano, Japan
Nagano, Japan
kby@shinshu-u.ac.jp tkomat@shinshu-u.ac.jp seiji@nii.ac.jp

Abstract

Pursuing humanlikeness has at least ﬁve critical problems. (1) Cost: in general, humanlikenessdemands powerful and highly functional hardware
and software, and highly integrated systems requiring top-grade experts both for development
and maintenance. All of them lead to cost overrun. (2) Performance: sometimes, humanlikenessforces performance to be compromised. For example, achieving quick turn-taking which humans
do in daily conversations forces automatic speech
recognizers, reasoners, etc. to be compromised toenable severe real-time processing. (3) Applicability: differences in cultures, genders, generations,situations limit the applicability of a humanlike
design because it often accompanies a rigid character. For example, Shiwa et al. (2008) succeeded
in improving users’ impression for slow responses
from a robot by using a ﬁller but obviously useof such a ﬁller is limited by social appropriateness. (4) Expectancy: humanlike systems induce
too much expectancy of users that they are as intelligent as humans. It will result in disappointments (Komatsu and Yamada, 2010) and may reduce users’ willingness to use systems. Keeping
high willingness is quite important from the viewpoint of both research (for collecting data fromusers to improve systems) and business (for continuously selling systems with limited functionality). (5) Risk: Although it is not veriﬁed, what iscalled the uncanny valley (Bartneck et al., 2007)
probably exists. It is commonly observed that people hate imperfect humanlike systems.
We try to avoid these problems rather than overcome them. Our position is positively exploring
non-humanlike spoken dialogue design. This pa-

We propose a non-humanlike spoken dialogue design, which consists of two elements: non-humanlike turn-taking andnon-humanlike acknowledgment. Two experimental studies are reported in this paper. The ﬁrst study shows that the proposed non-humanlike spoken dialogue design is effective for reducing speech collisions. It also presents pieces of evidence
that show quick humanlike turn-taking isless important in spoken dialogue systemdesign. The second study supports a hypothesis found in the ﬁrst study that user
preference on response timing varies depending on interaction patterns. Upon receiving these results, this paper suggests apractical design guideline for spoken dialogue systems.

1

Introduction

Speech and language are owned by humans.
Therefore, spoken dialogue researchers tend to
pursue a humanlike spoken dialogue. Only a few
researchers positively investigate restricted (i.e.,
non-humanlike) spoken dialogue design such as(Fernández et al., 2007).
Humanlikeness is a very important concept andsometimes it is really useful to design machines /interactions. Machines are, however, not humans.
We believe humanlikenss cannot be the dominantfactor, or gold-standard, for designing spoken dialogues.

Proceedings of SIGDIAL 2010: the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 176–184,
The University of Tokyo, September 24-25, 2010. c 2010 Association for Computational Linguistics

176per focuses on its two elements, i.e., decelerated
dialogues as non-humanlike turn-taking and an artiﬁcial subtle expression (ASE) as non-humanlike
acknowledgment1 , and presents two experimental
studies regarding these two elements. ASEs, deﬁned by the authors in (Komatsu et al., 2010), aresimple expressions suitable for artifacts, which intuitively notify users about artifacts’ internal states
while avoiding the above ﬁve problems.
In Section 2, the ﬁrst study, which was previously reported in (Funakoshi et al., 2010), issummarized and shows that the proposed nonhumanlike spoken dialogue design is effective for
reducing speech collisions. It also presents piecesof evidence that shows quick humanlike turntaking is less important in designing spoken dialogue systems (SDSs). In Section 3, the second
study, which is newly reported in this paper, showsa tendency supporting a hypothesis found in theﬁrst study that user preference on response timing
varies depending on interaction patterns. Upon receiving the results of the two experiments, a design
guideline for SDSs is suggested in Section 4.

2

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Figure 1: Interface robot with an embedded LED

tem’s performance is at human level. However, if
the system’s performance is below human level,high responsiveness might not be vital or even be
harmful. For instance, Hirasawa et al. (1999) reported that immediate overlapping backchannels
can cause users to have negative impressions. Kitaoka et al. (2005) also reported that the familiarityof an SDS with backchannels was inferior to thatwithout backchannels due to a small portion of errors even though the overall timing and frequency
of backchannels was fairly good (but did not comeup to human operators). Technologies are advancing but they are still below human level. We challenge the past work that took the ﬁrst approach.
The second approach is simple and stable against user differences and environmental
changes. Moreover, it can afford to employ more
powerful but computationally expensive speechprocessing or to build systems on small devices
with limited resources. A concern with this approach is debasement of user experience due topoor responsiveness as stated above. Another issue is speech collisions due to users’ followingup utterances such as repetitions. Slow responses
tend to induce such collision-eliciting speech.
This section shows the results of the experimentin which participants engaged in hotel reservation
tasks with an SDS equipped with an ASE-based
acknowledging method, which intuitively notiﬁed
a user about the system’s internal state (processing). The results suggest that the method can reduce speech collisions and provide users with positive impressions. The comparisons of evaluations
between systems with a slow reply speed and amoderate reply speed suggest that users of SDSs
do not care about slow replies. These results indicate that decelerating spoken dialogues is not a
bad idea.

Study 1: Reducing Speech Collisions
with an Artiﬁcial Subtle Expression
in a Decelerated Dialogue

An important issue in SDSs is the management ofturn-taking. Failures of turn-taking due to systems’ end-of-turn misdetection cause undesired
speech collisions, which harm smooth communication and degrade system usability.
There are two approaches to reducing speechcollisions due to end-of-turn misdetection. The
ﬁrst approach is using machine learning techniques to integrate information from multiple
sources for accurate end-of-turn detection in earlytiming. The second approach is to make a long interval after the user’s speech signal ends and before the system replies simply because a longer
interval means no continued speech comes. As
far as the authors know, all the past work takes
the ﬁrst approach (e.g., (Kitaoka et al., 2005;
Raux and Eskenazi, 2009)) because the second approach deteriorates responsiveness of SDSs. This
choice is based on the presumption that users prefer a responsive system to less responsive systems.
The presumption is true in most cases if the sys-

2.1 Experiment

1

In this paper, acknowledgment denotes that at the level 1of the joint action ladder (Clark, 1996), which communicatesthe listener’s identifying the signal presented by the speaker.

System An SDS that can handle a hotel reservation domain was built. The system was equipped

177detected speech onset
VAD tail margin wait intervaluser speech
short pauses

detected end-of-turnprocessing delay

USER
SYSTEMsystem speech
Y

X

time

blinking LED (artiﬁcial subtle expression)
Figure 2: Behavior of the dialogue system along a timeline(mean age 30.9) to one of the four conditions.
A reply speed depends on a wait interval forwhich the dialogue system awaits the next input.
Shiwa et al. (2008) showed that the best reply
speed for a conversational robot was one second.
Thus we chose 800 msec as the wait interval forthe moderate reply speed because an actual reply
speed was the accumulation of the wait interval
and a delay for processing a user request, and 800
msec is simply twice the default length (the VAD
tail margin) by which the Julius speech recognizer
recognizes the end of a speech. For the slow reply
speed, we chose 4 sec as the wait interval. Wait
intervals include the VAD tail margin.
Figure 2 shows how the system and the LED
work along with user speech. In this ﬁgure, a user
utters a continuous speech with a rather long pausethat is longer than the VAD tail margin but shorter
than the wait interval. If the system detects theend of the user’s turn and starts speaking within
the interval marked with an ‘X’, a speech collision
would occur. If the user utters a follow-up within
the interval marked with a ‘Y’, a speech collision
would occur, too. We try to suppress the former
speech collision by decelerating dialogues and thelatter by using a blinking light as an ASE.

with an interface robot with an LED attached toits chest (see Figure 1). Participants’ utteranceswere recognized by an automatic speech recognizer Julius2 , and interpreted by an in-house language understander. The robot’s utterances werevoiced by a commercial speech synthesizer. The
LCD monitor in Figure 1 was used only to show
reservation details at last.
Julius output a recognition result to the system
at 400 msec after an input speech signal ended, but
the system awaited the next input for a ﬁxed interval (wait interval, whose length is given as an experimental factor). If the system received an additional input, it awaited the next input for the sameinterval again. Otherwise, the system replied.
The LED started blinking at 1/30 sec evenintervals when a speech signal was detected andstopped when the system started replying. The
basic function of the blinking light expression issimilar to hourglass icons used in GUIs. A big
difference is that basically GUIs can ignore any input while they are showing those icons, but SDSs
must accept successive speech while it is blinking an LED. What we intend to do is to suppress
only collision-eliciting speech such as repetitions(we call them follow-ups) which are negligible
but difﬁcult to be automatically distinguished frombarge-ins. Barge-ins are not negligible.

Method The experiment was conducted in aroom for one participant at one time. Participants
Conditions and participants Two experimenentered the room and sat on a chair in front of atal factors were set-up, that is, the reply speed
desk as shown in Figure 1.
factor (moderate or slow reply speed) and the
The experimenter gave the participants instrucblinking light factor (with or without a blinking
tions so as to reserve hotel rooms ﬁve times bylight), resulting in four conditions:talking with the robot in front of them. All of themwere given the same ﬁve tasks which require them
A: slow reply speed, with a blinking light,
to reserve several rooms (one to three) at the same
B: slow reply speed, without a blinking light,
time. The meaning of the blinking light expresC: moderate reply speed, with a blinking light,sion was not explained to them. After giving the
D: moderate reply speed, without a blinking light.
instructions, the experimenter left the participants,
and they began tasks when the robot started to talkWe randomly assigned 48 Japanese participants
to them. Each task was limited to up to three min2
utes. After ﬁnishing the tasks, the participants anhttp://julius.sourceforge.jp/

178Impression on the dialogue and robot The participants rated 38 positive-negative adjective pairs(such as smooth vs. rough) for evaluating both thedialogue and the robot. The ratings are based on aseven-point Likert scale.
An ANOVA found a positive marginal signiﬁcance (p = 0.07) for the blinking light in the comfortableness factor extracted by a factor analysis
for the impression on the dialogue. In addition,an ANOVA found a positive marginal signiﬁcance(p = 0.07) for the slow reply speed in the modesty factor extracted by a factor analysis for the
impression on the robot. Surprisingly, no signiﬁcant negative effect for the slow reply speed wasfound.

swered a questionnaire. Figure 5 and Figure 6 in
the appendix show one of the ﬁve task instructions,and a dialogue on that task, respectively.
2.2 Results
Reply speeds Averages of observed reply
speeds were calculated from the timestamps intranscripts. They were 4.53 sec for the slow conditions and 1.42 sec for the moderate conditions.
Task completion The average number of completed tasks in the four conditions A, B, C, and D
were 4.00, 3.83, 3.83, and 4.33, respectively. An
ANOVA did not ﬁnd any signiﬁcant difference.
Speech collisions We counted speech collisions
for which the SDS was responsible, that is, thecases where the robot spoke while participants
were talking (i.e., end-of-turn misdetections). Of
course, there were speech collisions for which participants were responsible, that is, the cases whereparticipants intentionally spoke while the robot
was talking (i.e., barge-ins). These speech collisions were not the targets, hence they were not included in the counts.
Speech collisions due to participants’ backchannel feedbacks were not included, either. We
think that it is possible to ﬁlter out such feedback
because feedback utterances are usually very shortand variations are small. On the other hand, as
we mentioned above, it is not easy to automatically distinguish negligible speech such as repetitions from barge-ins. We want to suppress
only such speech negligible but hard to distinguishfrom other not negligible speech.
The number of observed speech collisions in
the four conditions A, B, C, and D were 5, 11,45, and 30, respectively. First we performed an
ANOVA on the number of collisions. The interaction effect was not signiﬁcant (p = 0.24). A signiﬁcant difference on the reply speed factor was
found (p < 0.005). This result conﬁrms that decelerating dialogues reduces collisions. The effect of the blinking light factor was not signiﬁcant(p = 0.60).
Next we performed a Fisher’s exact test (oneside) on the number of participants who had
speech collisions between the two conditions of
the slow reply speed (3 out of 12 for A and 8 outof 12 for B). The test found a signiﬁcant difference(p < 0.05). This result indicates that the blinking
light can reduce speech collisions by suppressing
users’ follow-ups in decelerated dialogues.

System evaluations The participants evaluatedthe SDS in two measures on a scale from 1 to 7,
that is, the convenience of the system and their
willingness to use the system. The greater theevaluation value is, the higher the degree of convenience or willingness.
The average scores of convenience in the four
conditions A, B, C, and D were 3.50, 3.17, 3.17,
and 3.92, respectively. Those of willingness were
3.58, 2.58, 2.83, and 3.42, respectively. ANOVAs
did not ﬁnd any signiﬁcant difference among thefour conditions both for the two measures.
Discussion on user preference The analysis of
the questionnaire suggests that the blinking light
expression gives users a comfortable impression
on the dialogue. The analysis also suggests thatthe slow reply speed gives users a modest impression on the interface robot. Meanwhile, no negative impression with a statistical signiﬁcance isfound on the slow reply speed.
Although no statistically signiﬁcant difference
is found between the four conditions, numbersof completed tasks and convenience are strongly
correlated. However, users’ willingness to use
the systems, which is the most important measure for systems, is inverted between condition
A and D. Convenience will be primarily dominated by what degree a user’s purpose (reserving
rooms) is achieved, thus, it is reasonable that convenience scores correlate with the number of completed tasks. On the other hand, willingness will
be dominated by not only practical usefulness but
also overall usability or experience. Therefore,
we can interpret that the improvements in impressions and reduction in aversive speech collisions

179speciﬁc cases. That is, we think in some situations users feel uncomfortable with slow verbal responses primordially, and those situations are suchas when users simply reply to systems’ yes-noquestions or greetings. Our hypothesis is that users
expect quick verbal responses (and hate slow verbal responses) only when users expect that it is not
difﬁcult for systems to understand their responses
or to decide next actions. This section reports theexperiment validating this hypothesis.

let condition A have the highest score for willingness. These results indicate that decelerating spoken dialogues is not a bad idea in contradiction
to the common design policy in human-computerinterfaces (HCIs), and they suggest to exploit merits provided by decelerating dialogues rather thanpursuing quickly responding humanlike systems.
Our ﬁnding contradicts not only the common design policy in HCIs but also the design policy in human-robot interaction found by
Shiwa et al. (2008), that is, the best response timing of a communication robot is at one second. We
think this contradiction is superﬁcial and is ascribable to the following four major differences between their study and our study.

3.1 Experiment
To validate the hypothesis described above, we
conducted a Wizard-of-Oz experiment using ﬁxed
scenarios. Participants engaged in short interactions with an interface robot and evaluated response timing of the robot. Three experimental
factors were interaction patterns, response timing(wait interval), and existence of a blinking light.

• They adopted a within-subjects experimental
design while we adopted a between-subjects
design. A within-subjects design makes subjects do relative evaluations and tends to emphasis differences.
• Their question was speciﬁc in terms of response timing. Our questions were overallratings of the system such as convenience.
• They assumed a perfect machine (Wizard-ofOz experiment). Our system was elaborately
crafted but still far from perfect.
• Our system quickly returns non-verbal responses even if verbal responses are delayed.

Interaction patterns Five interaction patterns
were setup to see the differences between situations. Each pattern consisted of three utterances.
The ﬁrst utterance was from the system. Upon receiving the utterance, a participant as a user of thesystem replied with the second utterance. Thenthe system responded after the given wait interval(1 sec or 4 sec) with the third utterance. Participants evaluated this interval between the second
utterance and the third utterance in a measure of
comfortableness.
The patterns with scenarios are shown in Figure 3. They will be referred to by abbreviations(PGG, QYQ, QNQ, PSQ, PLQ) in what follows.
Note that the scenarios are originally in Japanese.
Here, RequestS and RequestL mean a short request and a long request, respectively. YNQuestion and WhQuestion mean a yes-no-question anda wh-question, respectively. According to the hypothesis, we can predict that the reported comfortableness for the longer wait interval (4 sec)
are worse for short and formulaic cases such as
PGG and QYQ than for the long request case (i.e.,
PLQ). In addition, we can predict that the reported
comfortableness for longer intervals improves for
PLQ if the robot’s light blinks, while that does not
improve for PGG and QYQ.

From these differences, we hypothesize that response timing has no signiﬁcant impact on the usability of SDSs in an absolute and holistic context
at least in the current state of the art spoken dialogue technology, even though users prefer a system which responds quickly to a system which responds slowly when they compare them with eachother directly, given an explicit comparison metricon response timing with perfect machines.

3

Study 2: Uncovering Comfortablenessof Response Timing under Different
Interaction Patterns

Our conclusion in Section 2 is that SDSs do notneed to quickly respond verbally as long as theyquickly respond non-verbally by showing their internal states with an ASE, while many researchers
try to make them verbally respond as fast as possible. Decelerating a dialogue has many practicaladvantages as stated above.
However, through the experiment, we have also
suspected that this conclusion is not valid in some

System We used the same interface robot andthe LCD monitor as study 1. The experiment inthis study, however, was conducted using a WOZ
system.

180

Prompt-Greeting-Greeting (PGG)
S: Welcome to our Hotel. May I help you?
U: Hello.
S: Hello.
YNQuestion-Yes-WhQuestion (QYQ)
S: Welcome to our Hotel. Will you stay tonight?
U: Yes.
S: Can I ask your name?
YNQuestion-No-WhQuestion (QNQ)
S: Welcome to our Hotel. Will you stay tonight?
U: No.
S: How may I help you?
Prompt-RequestS-WhQuestion (PSQ)
S: Welcome to our Hotel. May I help you?
U: I would like to reserve a room from tomorrow.
S: How long will you stay?
Prompt-RequestL-WhQuestion (PLQ)
S: Welcome to our Hotel. May I help you?
U: I would like to reserve rooms with breakfast from tomorrow, one single room and one double room, nonsmoking and smoking, respectively.
S: How long will you stay?

not blink the LED. We refer to the former group(condition) as BL (Blinking Light, n=20) and thelater as NL (No Light, n=20). In summary, thisexperiment is within-subjects design with regard
to interaction patterns and response timing and is
between-subjects design with regard to the blinking light.
Method The experiment was conducted in aroom for one participant at one time. Participants
entered the room and sat on a chair in front of adesk as shown in Figure 1, but they did not wear
headphones this time.
The experimenter gave the participants instructions so as to engage in short dialogues with therobot in front of them. They engaged in each of
ﬁve scenarios shown in Figure 3 six times (threetimes with a 1 sec wait interval and three with
4 sec), resulting in 30 dialogues (5 × 3 × 2 = 30).
The order of scenarios and intervals was randomized. The existence and meaning of the blinking
light expression was not explained to them. They
were not told that the system was operated by a human operator, either. After giving the instructions,the experimenter left the participants, and they
practiced one time. This practice used a PromptRequestM-WhQuestion3 type scenario with a wait
interval of two seconds. Then, thirty dialogues
were performed. Short breaks were inserted after ten dialogues. Each dialogue proceeded as explained above.

Figure 3: Interaction patterns and scenarios
First the WOZ system presents an instruction to
the participant on the LCD monitor, which reveals
the robot’s ﬁrst utterance of the given scenario(e.g., “Welcome to our Hotel. May I help you?”)
and indicates the participant’s second utterance(e.g., “Hello.”). Two seconds after the participant
clicks the OK button on the monitor with a computer mouse, the system makes the robot utter theﬁrst utterance. Then, the participant replies, andthe operator of the system end-points the end ofparticipant’s speech by clicking a button shown inanother monitor for the operator in the room next
to the participant’s room. After the end-pointing,the system waits for the wait interval (one second
or four seconds) and makes the robot utter the thirdutterance of the scenario. One second after, thesystem asks the participant to evaluate the comfortableness of the response timing of the robot’s
third utterance on a scale from 1 to 7 (1:very uncomfortable, 4:neutral, 7:very comfortable) on the
LCD monitor.

3.2 Results
End-pointing errors End-pointing was done by
a ﬁxed operator. We obtained 1,184 dialogues outof 1,200 (= 30 × 40) after removing dialogues
in which end-pointing failed (failures were selfreported by the operator). We sampled 30 dialogues from the 1,184 dialogues and analyzed endpointing errors in the recorded speech data. The
average error was 84.6 msec (SD=89.6).
Comfortableness This experiment was designed to grasp a preliminary sense on our
hypothesis as much as possible with a limited
number of participants in exchange for abandonment of use of statistical tests, because thisstudy involved multiple factors and the interaction
pattern factor was complex by itself. Therefore,
in the following discussion on comfortableness,
we do not refer to statistical signiﬁcances.

Conditions and participants Forty participants(mean age 28.8, 20 males and 20 females) engaged
in the experiment. No participant had engaged instudy 1. They were randomly assigned to one of
two groups (gender was balanced). The groups
correspond to one of two levels of the experimental factor of the existence of a blinking light.
For one group, the robot blinked its LED when it
was waiting. For the other group, the robot did

3
The request utterance is longer than that of RequestS andshorter than that of RequestL.

181

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,+!

,+!

,+!

,+!

Figure 4: Comfortableness (Left: without a blinking light (NL), right: with a blinking light (BL))
twofold design guideline for SDSs, especially fortask-oriented systems. Some interaction-oriented
systems such as chatting systems are out of scopeof this guideline. In what follows, ﬁrst the guideline is presented and then a commentary on theguideline is described.

Figure 4 shows regression lines obtained fromthe 1,184 dialogues in the two graphs for NL and
BL (Detailed values are shown in Table 1). The
X axes in the graphs correspond to response timing, that is, the two wait intervals of 1 sec and
4 sec. The Y axes correspond to comfortableness
reported in a scale from 1 to 7. Obviously, with orwithout a blinking light effected comfortableness.
The results shown in the graphs support the predictions made in Section 3.1. The scores of PGG
and QYQ are worse than that of PLQ at 4 sec.
PGG and QYQ show no difference between NL
and BL. QNQ and PSQ show differences. PLQ
shows the biggest difference. In case of PLQ, thereported comfortableness at 4 sec shifted to almost the neutral position (score 4) by presenting ablinking light. This indicates that a blinking light
ASE can allay the debasement of impression due
to slow responses only in non-formulaic cases.
Interestingly, the blinking light expression attracted comfortableness scores to neutral both at
1 sec and at 4 sec. We can make two hypotheseson this result. One is that the blinking light expression has a negative effect which degrades comfortableness at 1 sec. The other is that the blinking
light expression makes participants difﬁcult to see
differences between 1 sec and 4 sec, therefore, reported scores converge to neutral. At this stage wethink that the later is more probable than the former because the scores of PGG and QYQ should
be degraded at 1 sec if the former is true.

4

The guideline(1) Never be obsessed with quick turn-taking
but acknowledge users immediately
Quick turn-taking will not recompense your efforts, resources inputted, etc. Pursue it only after accomplishing all you can do without compromising performance in other elements of dialoguesystems and only if it does not make system development and maintenance harder. However, quick(possibly non-verbal) acknowledgment is a requisite. You can compensate for the debasement ofuser experience due to slow verbal responses justby using an ASE such as a tiny blinking LED to
acknowledge user speech. No instruction about
the ASE is needed for users.
(2) Think of users’ expectations
Users expect rather quick verbal responses to theirgreetings and yes-answers. ASEs will be ineffective for them. Thus it is recommended to enable
your systems to quickly respond verbally to suchutterances. Fortunately it is easy to anticipate suchutterances. Greetings usually occur only at the beginning of dialogues or after tasks were accomplished. Yes-answers will come only after yes-noquestions. Therefore it will be able to implement
an SDS that quickly responds verbally to greetingand yes-answers both without increasing development / maintenance costs and without decreasing

A Practical Design Guideline for SDSs

Summarizing the results of the experiments presented in Section 2 and Section 3, we suggest a182

panied by an ASE will be applicable even to telephone interfaces by using an auditory ASE, whichis to be explored in future work.
The guideline is supported by ﬁndings in arather hypothetical stage. More experiments arenecessary to conﬁrm these ﬁndings. In addition,
the guideline is for the current transitory period
in which intelligence technologies such as automatic recognition, language processing, reasoning
etc. are below human level. In that sense, the contribution of this paper might be limited. However,this period will last until a decisive paradigm shift
occurs in intelligence technologies. It may comeafter a year, a decade, or a century.

recognition performance, etc.
However, you should keep in mind that tooquick verbal responses (0 sec interval or overlapping) may not be welcomed (Hirasawa et al., 1999;
Shiwa et al., 2008). They may also induce toomuch expectancy in users and result in disappointments to your systems after some interactions.
Commentary on the guideline
The guideline was constructed so as to avoid theﬁve problems pointed out in Section 1. The ﬁrst
point of the guideline is induced mainly from theresults of study 1, and the second point is induced
mainly from the results of study 2.
Although the results of study 2 indicate users
prefer quick responses to slow ones as presupposed in past literature, note that the experimentin study 2 is within-subjects design with regard to
the response timing factor and that within-subjects
design tends to emphasis differences as discussed
at the end of Section 2. The results of study 1
suggested that such an emphasized difference (i.e.,
preference for quick responses) has no signiﬁcant
impact on the usability of SDSs on the whole.

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Conclusion

K. Funakoshi, K. Kobayashi, M. Nakano, T. Komatsu,
and S. Yamada. 2010. Reducing speech collisions
by using an artiﬁcial subtle expression in a decelerated spoken dialogue. In Proc. 2nd Intl. Symp. New
Frontiers in Human-Robot Interaction.

This paper proposed a non-humanlike spoken dialogue design, which consists of two elements:
non-humanlike turn-taking and acknowledgment.
Two experimental studies were reported regardingthese two elements. The ﬁrst study showed that the
proposed non-humanlike spoken dialogue designis effective for reducing speech collisions. This
study also presented pieces of evidence that showquick humanlike turn-taking is less important inspoken dialogue system (SDS) design. The second
study showed a tendency supporting a hypothesis
found in the ﬁrst study that user preference on response timing varies depending on interaction patterns in terms of comfortableness. Upon receiving
these results, a practical design guideline for SDSs
was suggested, that is, (1) never be obsessed withquick turn-taking but acknowledge users immediately and (2) think of users’ expectations.
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portion of the deployed SDSs pie. Yet, the underlying concept: decelerated dialogues accom-

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Hotel Reservation Task 3
Reserve rooms as below
Stay
As specified with the orange-colored frameon the calendar
Room
Twin, 1 room, non-smoking
Double, 1 room, non-smoking

Figure 5: One of the ﬁve task instructions used in study 1

S:
U:
S:
U:
S:
U:
S:

Welcome to Hotel Wakamatsu-Kawada. May I help you?
I want to stay from March 10th to 11th.
What kind of room would you like?
One non-smoking twin room and one non-smoking double room.
Are your reservation details correctly shown on the screen?
Yes. No problem.
Your reservation has been accepted. Thank you for using us.

Figure 6: A successful dialogue observed with the task shown in Figure 5 (translated into English)

Table 1: Detailed comfortableness scores in study 2
Interaction pattern
PGG
QYQ
QNQ
PSQ
PLQ
Condition
NL
BL
NL
BL
NL
BL
NL
BL
NL
BL
mean
5.34 5.36 5.55 5.56 5.48 5.25 5.09 4.73 5.13 4.41
1 sec
s.d.
1.00 1.17 1.10 1.00 1.02 1.04 1.12 1.09 1.14 1.20
p-value
0.93
0.96
0.23
0.09
0.001
mean
3.12 3.16 3.37 3.36 3.28 3.52 3.43 3.52 3.54 3.83
4 sec
s.d.
0.94 1.04 0.78 0.93 0.76 0.93 0.81 0.87 0.95 0.87
p-value
0.83
0.98
0.14
0.59
0.08
p-values were obtained by two-sided t-tests between NL and BL. Those are shown just for reference.

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