Construction of Back-Channel Utterance Corpus for
Responsive Spoken Dialogue System Development
Yuki Kamiya† , Tomohiro Ohno‡ , Shigeki Matsubara†♮ , Hideki Kashioka♮†

Graduate School of Information Science, Nagoya University
Graduate School of International Development, Nagoya University
Furo-cho, Chikusa-ku, Nagoya, 464-8601, Japan♮
National Institute of Information and Communications Technology
3-5 Hikari-dai, Seika-cho, Soraku-gun, Kyoto, 619-0289, Japankamiya@el.itc.nagoya-u.ac.jp, {ohno,matubara}@nagoya-u.jp, hideki.kashioka@nict.go.jp‡

Abstract
In spoken dialogues, if a spoken dialogue system does not respond at all during user’s utterances, the user might feel uneasy because theuser does not know whether or not the system has recognized the utterances. In particular, back-channel utterances, which the system
outputs as voices such as yeah and uh huh in English have important roles for a driver in in-car speech dialogues because the driver
does not look towards a listener while driving. This paper describes construction of a back-channel utterance corpus and its analysis todevelop the system which can output back-channel utterances at the proper timing in the responsive in-car speech dialogue. First, weconstructed the back-channel utterance corpus by integrating the back-channel utterances that four subjects provided for the driver’s
utterances in 60 dialogues in the CIAIR in-car speech dialogue corpus. Next, we analyzed the corpus and revealed the relation between
back-channel utterance timings and information on bunsetsu, clause, pause and rate of speech. Based on the analysis, we examined thepossibility of detecting back-channel utterance timings by machine learning technique. As the result of the experiment, we confirmedthat our technique achieved as same detection capability as a human.

1. Introduction
With the recent advances in speech recognition technologies, a considerable number of studies have been conductedon spoken dialogue systems such as car navigation systems.
Since these systems are designed to accurately respond touser’s requests, their response is done after a user utterance
is completely finished. However, in spoken dialogues, if
a system does not respond at all during user’s utterances,
the user might feel uneasy because the user does not knowwhether or not the system has recognized the utterances.
In human-to-human dialogues, a listener informs a speakerthat the listener is listening to speaker’s speech not only
by replaying after the speaker’s utterance but also by performing actions such as back-channel utterances, laughing
or nods during the utterance. In particular, back-channel utterances, which the system outputs as voices such as “yeah”
and “uh huh” in English, have important roles for a driverin in-car speech dialogues because the driver does not looktowards a listener while driving.
This paper describes construction of a back-channel utterance corpus and its analysis to develop the system which
can output back-channel utterances at the proper timing
during user’s utterances in the responsive in-car speech dialogue. Until now, although there are several researches
for developing the systems which output back-channel utterances (Fujie et al., 2004; Kopp et al., 2007; Ward andTsukahara, 2000), they have not comprehensively analyzed
the timings at which back-channel utterances can be provided. Our research tries to comprehensively reveal thetimings at which back-channel utterances can be provided
during speaker’s utterances. First, we constructed the backchannel utterance corpus by recording back-channel utterances of multiple subjects and then integrating each timing.

Next, we analyzed the corpus and revealed the relation between back-channel utterance timings and information on
bunsetsu1 , clause, pause and rate of speech.
The rest of this paper is organized as follows. The next section explains a back-channel utterance. The constructionof a back-channel utterance corpus and its analysis are reported in Section 3 and 4, respectively. Section 5 presents
examination on detection of back-channel utterance timings. Section 6 concludes the paper.

2.

Back-channel utterance

The definition of Japanese back-channel utterance has
been discussed by various researchers. Maynard defineda Japanese back-channel utterance as “a short expression
which a listener sends while a speaker is speaking” (Maynard, 1989). In addition, Horiguchi defined it as “signwhich informs a speaker that a listener is listening to
speaker’s utterances” (Horiguchi, 1997). We can say that
it is important to provide back-channel utterances at theproper timings. Because a speaker might wonder if a listener surely listens to and comprehends his/her speech if
back-channel utterances are unnecessarily provided.
The proper timings at which back-channel utterances
should be provided differ depending on the participants andthe situations. In case of making a spoken dialogue systemgenerate back-channel utterances, it is required to change
the timing at which back-channel utterances are outputteddepending on the user or situation. In our research, we
think of detecting comprehensively the timings at which
1

Bunsetsu is a linguistic unit in Japanese that roughly corresponds to a basic phrase in English. A bunsetsu consists of one
independent word and zero or more ancillary words.

subject 1
subject 2
subject 3
subject 4

232
217
65
136

bunsetsu NO.
0
1
2

Table 1: Number of back-channel utterances3back-channel utterances can be provided, and then deciding the proper back-channel utterance timing depending on
the user’s preference and the situation among the detected
timings. This can prevent the system from outputting backchannel utterances at the unnatural timings.

4

3. Construction of back-channel utterance
corpusIn order to analyze comprehensively the timings at whichback-channel utterances can be provided, we constructed aback-channel utterance corpus by integrating back-channelutterances which multiple subjects provided.
3.1. Recording and transcription of back-channelutterances
We recorded back-channel utterances which four subjects
provided for driver’s utterances (297 turns in 60 dialogues)in the CIAIR in-car speech dialogue corpus (Kawaguchi etal., 2004). We limited the utterance of the subjects to only
“はい hai (yeah),” and ordered them to provide as many
back-channel utterances as possible at non-unnatural timings. In addition, we played the subjects the sounds ofdriver’s utterances turn-by-turn, and ordered them to provide back-channel utterances during the driver’s utterancesof each turn. Table 1 shows the number of back-channelutterances by each subject.
We transcribed the driver’s utterances and the timings at
which each subject provided back-channel utterances basedon the recorded speech. Figure 1 shows an example ofthe transcription. Each line means a morpheme or a pause
in driver’s utterances. Each line contains information on
the start-end time and whether or not a back-channel utterance was provided at the place. In addition, information on
the part-of-speech, bunsetsu boundary and clause boundary are described in case of a morpheme. In our research,a morpheme or a pause2 is thought of as a basic segment
for analysis. By analyzing which segment a back-channelutterance was provided at, we reveal the characteristic ofback-channel utterance timings. Here, information on morphological analysis, clause boundary and time of each basic segment were provided by ChaSen (Matsumoto et al.,1999), CBAP (Kashioka and Maruyama, 2004) and Julius(Lee et al., 2001), respectively.
3.2. Integration of back-channel utterances by eachsubject
As shown by Figure 1 and Table 1, the number and timingof back-channel utterances are different from each subject.
2

If the length of a pause is over 200ms, the pause is divided
into two basic segments: the initial 200ms pause and the leftpause.

5morpheme or pause

start time end timeclause boundaryと to と “感動詞-フィラー (interjection)”pause [pause less than 200 ms]
豪華 goka 豪華 “名詞-普通名詞 (noun)”
な na だ “助動詞 (auxiliary verb)”
フランス furansu フランス “名詞-固有名詞 (noun)”
料理 ryori 料理 “名詞-普通名詞 (noun)”が ga が “助詞-格助詞 (particle)”
食べ tabe 食べる “動詞-一般 (verb)”
たい tai たい “助動詞 (auxiliary verb)”ん n ん “助詞-準体助詞 (particle)”です desu です “助動詞 (auxiliary verb)”
けども kedomo けども “助詞-接続助詞 (particle)” compound clause-keredomo
pause [initial 200ms pause]
pause [left pause]
この kono この “連体詞 (adnominal)”
辺 hen 辺 “名詞-普通名詞 (noun)”
で de だ “助動詞 (auxiliary verb)”
pause [initial 200ms pause]
pause [left pause]
…….

0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
1
0

0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0

0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0

0.03
0.18
0.29
0.62
0.75
1.23
1.54
1.65
1.92
2.17
2.20
2.43
2.95
3.16
3.65
3.92
4.23
4.55
4.76

0.17
0.28
0.61
0.74
1.22
1.53
1.64
1.91
2.16
2.19
2.42
2.94
3.15
3.64
3.91
4.22
4.54
4.75
4.85

Starting from the left, whether or not subject 1, 2, 3, 4 provided
a back-channel utterance in each segment.
(1: provided, 0: did not provide)

Figure 1: Sample of transcription of driver’s utterances andsubjects’ back-channel utterances

bunsetsu NO.

morpheme or pause

0 と to と “感動詞-フィラー (interjection)”pause [pause less than 200 ms]
1 豪華 goka 豪華 “名詞-普通名詞 (noun)”
な na だ “助動詞 (auxiliary verb)”
2 フランス furansu フランス “名詞-固有名詞 (noun)”
料理 ryori 料理 “名詞-普通名詞 (noun)”が ga が “助詞-格助詞 (particle)”
3 食べ tabe 食べる “動詞-一般 (verb)”
たい tai たい “助動詞 (auxiliary verb)”ん n ん “助詞-準体助詞 (particle)”です desu です “助動詞 (auxiliary verb)”
けども kedomo けども “助詞-接続助詞 (particle)”pause [initial 200ms pause]
pause [left pause]
4 この kono この “連体詞 (adnominal)”
辺 hen 辺 “名詞-普通名詞 (noun)”
で de だ “助動詞 (auxiliary verb)”
pause [initial 200ms pause]
pause [left pause]
5 …….

clause boundary

compound clause-keredomo

start time end time
0
0
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
1
0

0.03
0.18
0.29
0.62
0.75
1.23
1.54
1.65
1.92
2.17
2.20
2.43
2.95
3.16
3.65
3.92
4.23
4.55
4.76

0.17
0.28
0.61
0.74
1.22
1.53
1.64
1.91
2.16
2.19
2.42
2.94
3.15
3.64
3.91
4.22
4.54
4.75
4.85

Starting from the left, whether or not subject 1, 2, 3, 4 provided
a back-channel feedback in each segment.
(1: provided, 0: did not provide)

Figure 2: Sample of back-channel utterance corpus constructed by the integration

Our back-channel utterance corpus was constructed by integrating the back-channel utterances which four subjects
provided. This integration was performed, by focusing on
the timing at which each subject provided a back-channelutterance as voice (hereafter, output timing) and the timing
at which each subject thought to provide the back-channelutterance in one’s head (hereafter, inspiration timing), in
the following order:
1. In case that the output timings are different but theinspiration timings are thought to be same, thoseback-channel utterances are eliminated except a backchannel utterance which the most subjects provided at
the same output timing.
2. All back-channel utterances which were left after theabove procedure are adopted as the meaningful output
timing.
Figure 2 shows a sample of the back-channel utterance corpus constructed by integrating back-channel utterances offour subjects which are shown in Figure 1. In this integration, the inspiration timings of back-channel utterancesdialogue
turnbunsetsu
morphemepause
back-channel utterance

60
297
1,478
3,178
756
324

Table 2: Size of back-channel utterance corpus

dialogueturnbunsetsu
morphemepause
back-channel utterance

50
250
1,227
2,656
618
265

Table 3: Size of analysis data

which were provided at “けども kedomo (although) (2.432.94 sec)” and “left pause (3.16-3.64 sec)” in Figure 1 arejudged to be same and then only the back-channel utterance
which was provided at “けども kedomo (although) (2.432.94sec)” is left and the other is eliminated.
Table 2 shows the size of the back-channel utterance corpus
which was constructed by this means.
3.3. Evaluation of back-channel utterance corpus
To evaluate the validity of the back-channel utterance corpus constructed in the previous section, we conducted a
subjective experiment. In this experiment, an evaluator listened to the mixed sounds of the driver’s speech and the
back-channel utterances created by using a speech synthesizer according to the corpus, and then, evaluated whether
or not the timing of each back-channel utterance is unnatural. We created the sound of a listener’s back-channel utterance so that the back-channel utterance was outputted at
50msec after the start time of the basic segment at which
the back-channel utterance was provided. Here, we createdthe back-channel utterance “はい hai (yeah)” by using the
speech synthesizer “HitVoice” produced by Hitachi, Ltd.
Among 324 back-channel utterances in the constructed corpus, 34 back-channel utterances, which occupy 10.5% of
the total, were judged as unnatural. Considering that the
back-channel utterances which the subjects have to hearare all the same, the availability of the constructed backchannel utterance corpus is accepted as a reasonable degree.

4. Characteristic analysis of back-channelutterance timings
In our research, we plan to adopt a statistical approach todetect the timing at which a back-channel utterance can beprovided. To decide the available features in a statistical
approach, we analyzed the characteristic of back-channelutterance timings by using the corpus constructed in theprevious section. We focused on bunsetsu and clause as
linguistic information and a pause and rate of speech as

particleinterjection
auxiliary verb
noun
conjunction
adverbadjectiveverb
suffixpronoun

25.2%
8.5%
27.5%
24.0%
5.5%
9.5%
0.0%
8.3%
9.1%
0.0%

(102/405)(21/246)(28/102)(18/75)(3/55)(3/42)(0/17)(1/12)(1/11)(0/10)

Table 4: POS of the final morpheme of a bunsetsu and its
rate at which back-channel utterances are provided

phonetical information, and investigated the relations between them and back-channel utterance timings. In theanalysis, we used 50 dialogues in the back-channel utterance corpus. Table 3 shows the size of the analysis data.
There were 3,274 basic segments, consisting of 2,656 morphemes and 618 pauses, in the 50 dialogues. Among them,a back-channel utterance was provided in 265 basic segments. The rate at which back-channel utterances are provided was 8.1%. Here, this numerical value means the rate
at which a back-channel utterance was averagely provided
in a basic segment. If the rate at which back-channel utterances are provided in the basic segments having a characteristic is higher than this numerical value 8.1%, it means
back-channel utterances are easy to be provided in the basic
segments.
4.1. Bunsetsu and back-channel utterance timing
Back-channel utterances are considered to be provided bya listener when the listener understood the utterances of the
speaker to some extent because back-channel utterances express the contents understanding. On the other hand, a bunsetsu is the smallest meaningful unit in Japanese. Therefore, a back-channel utterance tends to be provided rightafter a bunsetsu is uttered.
There were 977 bunsetsus except the final bunsetsu of turns
in the analysis data. Among them, a back-channel utterance
was provided in the basic segments right after 178 bunsetsus. The rate at which back-channel utterances are provided
was 18.2%. The rate at which back-channel utterances areprovided in the basic segments right after bunsetsus washigher than the rate in all basic segments (8.1%), and thus,
we confirmed that back-channel utterances tend to be provided in the basic segments right after bunsetsus.
Next, we analyzed the difference between the types of abunsetsu. Table 4 shows the rate at which back-channel utterances are provided in the basic segments right after eachtype of a bunsetsu, which is classified by the POS of the
final morpheme of a bunsetsu. A back-channel utterance iseasy to be provided in the basic segments right after a bunsetsu of which the final morpheme is a particle, an auxiliaryverb or a noun.
4.2. Clause and back-channel utterance timing
Since a clause is a more meaningful unit than a bunsetsu, aback-channel utterance is considered to tend tobe provided

5.1%
8.6%
67.6%
7.4%
12.0%
42.9%
7.1%
40.0%

(6/118)(3/35)(23/34)(2/27)(3/25)(6/14)(1/14)(4/10)

50

rate at which back-channelutterances are provided (%)

interjection
quotation clausecompound clause -keredomo
conjunction
topicalized element -wa
compound clause -te
continuous clauseindirect question clause

4.3. Pause and back-channel utterance timing
Since a back-channel utterance has the function to demandutterances from a partner, a back-channel utterance is considered to tend to be provided in a pause.
Among 618 pause segments, a back-channel utterance wasprovided in 102 pause segments. The rate at which backchannel utterances are provided in the pause segments was
16.5% and higher than the rate in all basic segments (8.1%).
We confirmed that a back-channel utterance tends to be provided in the pause segments.
4.4. Rate of speech and back-channel utterance
timingSince a back-channel utterance has the function to demandutterances from a partner, a back-channel utterance is considered to tend to be provided when the rate of speech of
the parter becomes slow. We measured the rate of speech(mora/second) of each morpheme in drivers’ utterances andclassified all morphemes into the two classes: the fasterclass and the slower class than the average rate of speechof a morpheme. We investigated the rate at which backchannel utterances are provided in the basic segments rightafter morphemes in each of the two classes. Here, theaverage rate of speech of a morpheme was calculated foreach driver in consideration of the difference between persons. In case of a morpheme of which the rate of speech3

In our research, we used the types of clause boundaries defined by the Clause Boundary Annotation Program (Kashioka andMaruyama, 2004).

30
20
10
0

Table 5: Type of clauses and its rate at which back-channelutterances are provided

right after a clause.
There existed 341 clauses except final clauses of turns in theanalysis data. Among them, a back-channel utterance wasprovided in the basic segments right after 66 clauses. The
rate at which back-channel utterances are provided in thebasic segments right after clauses was 19.4%, and higher
than the rate in all basic segments (8.1%) and in the basic
segments right after bunsetsus (18.2%). Therefore, we confirmed that back-channel utterances tend more strongly tobe provided in the basic segments right after clauses.
Next, we analyzed the difference between the types3 ofclauses. Table 5 shows the rate at which back-channel utterances are provided in the basic segments right after eachtype of a clause. The rate for the basic segment right after “compound clause -keredomo” was high. The ease-ofprovision of back-channel utterances was different between
the types of clauses.

40

-8

-7

-6

-5

-4

-3

-2

-1

difference between the rate of speech of each morphemeand the average (mora/sec)

Figure 3: Rate of speech and rate at which back-channelutterances are provided

is faster than the average, the rate at which ack-channel utterances are provided in the basic segments right after themorpheme was 5.7% (99/1,746). In case of a slower morpheme, the rate at which back-channel utterances are provided in the basic segments right after the morpheme was
12.9% (165/1278). From this, we could see that if the rateof speech of a morpheme became slower than the averagerate of speech, a back-channel utterance became easy to beprovided in the basic segment right after the morpheme.
Furthermore, we classified all morphemes according to thedifference between the rate of speech of each morphemeand the average rate of speech on a 1 mora-per-second basis, and investigated the rate at which back-channel utterances are provided in the basic segments right after morphemes in each of the classes. The result is shown in Figure 3. The slower the rate of speech of a morpheme was,the higher the rate at which back-channel utterances areprovided became. We could see that a back-channel utterance tended to be provided more strongly when the rate ofspeech of a morpheme became slower.

5.

Examination of detection of back-channelutterance timings

We examined the possibility of automatically detecting
back-channel utterance timings by a machine learning technique based on the analysis described in Section 4.
5.1. Technique for detecting back-channel utterance
timings
Our research has an assumption that a basic segment in asequence of basic segments (s1 · · · sn ) of a turn is sequentially inputted. In our technique, whenever a basic segment
is inputted, it is judged whether or not a back-channel utterance can be provided in the input basic segment by using
Support Vector Machine (SVM). Table 6 shows the features
used by our SVM in judging whether or not a back-channelutterance is provided in a basic segment si . These features
can be obtained from s1 · · · si−1 , which is a sequence of basic segments right before the input basic segment si . Here,our research assumes that the information of each basic segment such as morphological information, clause information and time information can be obtained after the basic

1. whether or not si−1 is the final morpheme of a bunsetsu
2. POS of si−1 (if 1. is ture)
3. whether or not si−1 is the final morpheme of a clause
4. type of the clause to which si−1 belongs (if 3. is ture)
5. whether or not si−1 is the initial 200ms pause segment
6. whether or not the rate of speech of si−1 is slower than the average
7. difference between the rate of speech of si−1 and the average (if 6. is true)
8. time length from the start time of the previous back-channel utterance (the start time of s1 if there were no
previous back-channel utterances) to the start time of si .
Table 6: Features used by our SVM

segment is inputted.
5.2. Experiment and discussion
We conducted an experiment on detecting back-channel utterance timings and examined the possibility of the detection. Among the corpus constructed in Section 3, we used
50 dialogues which were the analysis data in Section 4 aslearning data, and the remaining 10 dialogues as test data.
LibSVM(Chang and Lin, 2001) was used with the default
option as the SVM tool. For comparison, we collected thedata of back-channel utterances by another subject, which
was not the subjects of the construction described in Section 3. In the evaluation, we obtained recall and precision
which were respectively defined as follows:
precision =recall =# of correctly provided BCU s# of provided BCU s# of correctly provided BCU s# of BCU s in the correct data

Here, BCUs mean back-channel utterances. If a basic segment in which a back-channel utterance was provided in
the detection result matches that in the correct data, we
judged that the back-channel utterance has been correctly
provided.
Table 7 shows the experimental result. In the result by
the subject, the f-measure was 33.8%. This indicates thedifficulty of detecting the precisive back-channel utterance
timings. The detection performance of our technique wasslightly below that of the result by the subject.
On the other hand, in a spoken dialogue system, we thinkthat if the detected timing has only the slight difference
from the correct timing, the detected timing can be accepted. Therefore, we reevaluated a provided back-channelutterance as a correctly provided one if the difference between the detected timing and the timing of the correct
data was within 200ms. Table 8 shows the reevaluation
result. Our technique achieved a comparatively-high precision (70.8%). By expanding the learning data, we thinkback-channel utterance timings can be detected at the allowable level.

6.

Conclusions

In this paper, first, we constructed the back-channel utterance corpus, which included 324 back-channel utterances,by integrating the back-channel utterances that 4 subjects

our method
subject

precision
54.2%(13/24)
63.6%(14/22)

recall22.0%(13/59)23.7%(14/59)f-measure
31.3%
33.8%

Table 7: Experimental result

our method
subject

precision
70.8%(17/24)
72.7%(16/22)

recall28.8%(17/59)27.1%(16/59)f-measure
41.0%
39.5%

Table 8: Reevaluation result

provided for the utterances of the driver’s 297 turns in 60
dialogues. Next, we revealed the characteristic of backchannel utterance timings, by analyzing the constructed
corpus. As the result, we found the following things:• The rate at which back-channel utterances are provided in the basic segments right after bunsetsus was
18.2%. Since this rate was higher than 8.1%, which isthe average rate at which back-channel utterances areprovided we can recognize the tendency that a backchannel utterance is easier to be provided in a basic
segment right after a bunsetsu. In particular, a backchannel utterance is much easier to be provided in thebasic segments right after bunsetsus of which the final
morpheme is a particle or auxiliary.
• The rate at which back-channel utterances are provided in the basic segment right after a clause was
19.4% and higher than the average rate 8.1%. We
found that a back-channel utterance is easier to be provided in the basic segment right after a clause. In particular, a back-channel utterance tends to be provided
in the basic segment right after a clause labeled “compound clause-keredomo.”• The rate at which back-channel utterances are provided in pause segments was 16.5% and higher than
the average rate 8.1%. Therefore, a back-channel utterance tends to be provided in a pause segment.
• When the rate of speech of a morpheme was slowerthan the average rate of speech, the rate at which backchannel utterances are provided in the basic segments
right after such a morpheme was 12.9%. A backchannel utterance tends to be provided in the basic
segment right after a morpheme of which the rate ofspeech is slower than the average rate of speech.
As the result of the experiment on detecting back-channelutterances based on the above analysis, we confirmed that
our technique achieved as same detection capability as ahuman.
In the future, we plan to analyze the characteristic of backchannel utterances by using the prosodic information, andthen, develop the system which can comprehensively detect
the back-channel utterance timings.

Acknowledgments
This research was supported in part by the Grant-in-Aid for
Challenging Exploratory Research (No.21650028) of JSPS.

7.

References

C. C. Chang and C. J. Lin, 2001. LIBSVM: a library forsupport vector machines. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm.
S. Fujie, K. Fukushima, and T. Kobayashi. 2004. A conversation robot with back-channel feedback function basedon linguistic and nonlinguistic information. In Proceedings of 2nd International Conference on Autonomous
Robots and Agents (ICARA 2004), pages 379–384.
S. Horiguchi. 1997. Nihongo kyoiku to kaiwa bunseki(Japanese conversation by learners and native speakers).
Kuroshio, Tokyo. (In Japanese).
H. Kashioka and T. Maruyama. 2004. Segmentation ofsemantic units in Japanese monologues. In Proceedings of International Conference on Speech and Language Technology (ICSLT 2004) and the International
Committee for the Co-ordination and Standardization of
Speech Databases and Assessment Techniques (OrientalCOCOSDA 2004), pages 87–92.
N. Kawaguchi, S. Matsubara, Y. Yamaguchi, K. Takeda,
and F. Itakura. 2004. CIAIR in-car speech database. In
Proceedings of 8th International Conference on Spoken
Language Processing (ICSLP 2004), pages 2789–2792.
S. Kopp, T. Stocksmeier, and D. Gibbon. 2007. Incremental multimodal feedback for conversational agents. Intelligent Virtual Agents, 4722:139–146.
A. Lee, T. Kawahara, and K. Shikano. 2001. Julius– an open source real-time large vocabulary recognition engine. In Proceedings of 7th European Conference on Speech Communication and Technology (EUROSPEECH 2001), pages 1691–1694.
Y. Matsumoto, A. Kitauchi, T. Yamashita, and Y. Hirano.
1999. Japanese morphological analysis system ChaSen
version 2.0 manual. In NAIST Technical Report, NAISTIS-TR99009.
S. K. Maynard. 1989. Japanese conversation : selfcontextualization through structure and interactional
management. Ablex, Norwood, NJ.
N. Ward and W. Tsukahara. 2000. Prosodic features which
cue back-channel responses in English and Japanese.
Journal of Pragmatics, 32(8):1177–1207.