Proceedings, APSIPA Annual Summit and Conference 2020

7-10 December 2020, Auckland, New Zealand

Spoken Dialog Training System for
Customer Service Improvement
Yuta Sano∗ , Chee Siang Leow∗ , Soichiro Iida† , Takehito Utsuro† , Junichi Hoshino † ,Akio Kobayashi ‡ and Hiromitsu Nishizaki∗∗ University of Yamanashi, Kofu, Japan

E-mail: {sanoyuta,cheesiang leow}@alps-lab.org, hnishi@yamanashi.ac.jp, Tel/Fax: +81-55-220-8361† University of Tsukuba, Tsukuba, Japan
E-mail: iida.soichiro.sy@alumni.tsukuba.ac.jp, utsuro@iit.tsukuba.ac.jp, jhoshino@esys.tsukuba.ac.jp‡ Tsukuba University of Technology, Tsukuba, Japan
E-mail: a-kobayashi@a.tsukuba-tech.ac.jp
Abstract—In the hospitality industry, including convenience
stores and airport service counters, operational staff must be
trained to serve customers satisfactorily and to avoid problems
with them. This study investigates a spoken dialog training
system for improving customer service by operational staff. In a
conventional spoken dialog system, a system user uses the dialog
system as a customer, and the dialog agent assists the system user
in fulfilling his or her requirements. In our system, in contrast,
the dialog agent plays the role of the customer. Consequently,
the behaviors of the human and the customer are opposite tothose in a traditional dialog system, and there has thus far been
no research on such systems. This paper introduces a prototype
of such a system that we have developed and describes a simple
experiment. The results of the experiment confirm the usefulnessof our system for hospitality training.

I. I NTRODUCTION
The Japanese word “Omotenashi” refers to the highest
possible level of customer service, and many foreigners who
visit Japan say they are impressed by Japanese hospitality.
Japan provides the best customer service of any country.
Even convenience stores and supermarkets, not just high-classrestaurants and hotels, emphasize the spirit of hospitality. In
fact, in Japan and other countries, customer satisfaction tends
to be higher in stores with excellent customer service [1]. In
contrast, most customers leave a shop or restaurant when theyencounter bad service, and they tend to return to a shop or
restaurant when they receive good service [2]. The level ofcustomer service is very important in business.
Consequently, in the hospitality industry, including convenience stores and service counters, operational staff must
be required to obtain good customer service skills, such assmooth customer service, customer satisfaction, and avoidanceof problems with customers. For this reason, people newly employed in the service industry often receive customer service
training.
Conventional customer service training involves learning
by reading a customer service manual [3] or by having an
instructor in on-the-job training (OJT) [4]. Operational staff
trained using a manual, might not be able to respond suitably
to situations that are not discussed in the manual. In addition,a manual cannot reproduce a real working environment. In

978-988-14768-8-3/20/$31.00 ©2020 APSIPA

addition, OJT involves high cost and time expenditure foracquiring instructors (trainers).
To solve these problems, this paper proposes a spoken
dialog training system that does not require human instructors
and can be used exclusively by trainees. A virtual agentbased interview training system [5] has been proposed for use
as a computer-assisted training system. In addition, a debate
training system [6] that uses automatic speech recognition
and gestures has been studied as a training system similar toour customer service training system. However, even though
this debate training system and our customer service training
system are similar in that both systems use dialog management modules, the two systems’ designs differ fundamentally.
For example, our training system introduces dynamic dialog
management based on scenario data for flexible response tovarious situations.
It can be expected that a training system based on speechinteraction with a virtual agent would be very effective in
improving customer service. In the hospitality industry, it isnecessary to increase customer satisfaction or to solve theirproblems through conversation. Thus, dialog-based training isessential. The system we developed is easy to handle and canbe used for training not only in assuring customer hospitality
but also in handling customer complaints. The hospitality
industry covers a wide range of industries, including retail
outlets such as convenience stores and restaurants. A systemthat provides customer service training for virtual customers
in many situations in a virtual space would enable trainees totrain readily and improve their customer service.
Many spoken dialog systems have been studied including,for example, purpose-oriented speech dialog systems [7], suchas a ticketing or reservation systems [8], [9], information
providing speech dialog systems such as sightseeing guides[10], and chat speech dialog systems [11] without a fixed topic,
such as Apple Siri [12]. Previous purposive and informative
spoken dialog systems have included a beneficiary, i.e., aperson who issues a request to the virtual agent in the
dialog system. The virtual agent assists in satisfying humanrequirements. However, in the spoken dialog system developedin this paper, the roles of the virtual agent and the human using

403

APSIPA-ASC 2020

Proceedings, APSIPA Annual Summit and Conference 2020

7-10 December 2020, Auckland, New Zealandthe system are opposite to those of conventional speech dialog
systems. In other words, the virtual agent is a “customer,” andthe human (trainee) converses with the virtual agent to satisfy
the customer’s requirements. The trainee learns the correctmanner in which to serve customers by using appropriate
speech in response to the various behaviors and actions ofvirtual customers. This reversal of the roles of virtual agent andtrainee differs substantially from conventional speech dialog
systems, and there has been no research on such systems.
Our goal is to develop a spoken dialog system for hospitality
training. This paper reports on the development of the first
prototype of such a system and experimental confirmation of
its usefulness in actual customer service. Traditional spoken
dialog systems have used dialog management based on thestate of the dialog. Those systems use a rule-based or reinforcement learning-based agent [13], [14], such as a partiallyobservable Markov decision process (POMDP) [15] to transition from one state to another state depending on speech
input. In recent years, end-to-end (E2E) speech dialog systems
have been studied [16], [17], [18], such as those that generate
response sentences directly from input utterances. Yang et al.
[19] also proposed an E2E-based dialog manager. Consideringspeech interaction for customer service training, E2E-based
speech interaction control might limit variation in training
situations. In this paper, we incorporate a traditional rule-based(modular-based) dialog management method. In addition, it iseasier to manage dialog states in a rule-based system than in
an E2E system. Since the training scenarios are expected to beprepared by humans, we think it would be easier to develop
them. Although the system in this paper is a prototype, it
has two characteristic features: first, it can provide customer
service training in various job situations using training dialog
scenarios provided in comma-separated value (CSV) format;
second, it can be used by a single trainee without any human
instructors.
The developed prototype system was used by subjects withpractical experience, who were then asked to evaluate it after
they participated in customer service training using the system.
The results confirmed that customer service training using the
dialog system might be useful in real work situations. The
contribution of this paper is that we have built a spoken dialog
training system for customer service improvement for the
first time that can train customer interaction in the hospitality
industry, and we have asked subjects with practical experience
to confirm that the training using this system can be usedin practice. The experimental results showed that our simple
dialog training system could be used for practical customer
service training as well.
The remainder of this paper is organized as follows. Section
2 introduces the design of the spoken dialog system, thespecific dialog management flow, and the training scenario.
Section 3 describes and discusses the experiment. Section 4summarizes the paper.

II. S POKEN D IALOG T RAINING S YSTEM FOR C USTOMERS ERVICE I MPROVEMENT
A. System outline
Fig. 1 shows the configuration of the spoken dialog training system for customer service improvement. The system
configuration is almost the same as that of a conventional
modular-based speech dialog system. After speech recognitionof the trainee’s speech, morphological analysis is applied to
the result. Then, the analyzed text is inputted to the dialog
management module. That module updates the state of the
dialog according to the content of the speech (recognized text)
and sends the response of the virtual agent (customer) basedon the updated status to the system control module. Whenthat module receives a response from the dialog management
module, it instructs the synthesis voice control module to playthe voice of the virtual agent. In addition, it also sends thecustomer agent’s line of dialog, the explanation of the current
dialog status, and the customer service advice to the graphicaluser interface (GUI).
Our dialog system was developed on Unity ver.2018.3.5f1[20], a game development platform, and the dialog management module was implemented based on “Pytransitions,” [21]a finite state machine management platform implemented in
Python. We used OpenJTalk [22] as our speech synthesizer.
We describe each function in detail.
B. Speech recognition
The automatic speech recognition (ASR) system uses Windows online speech-to-text API provided by Microsoft, which
can be used in a Unity platform. The trainer’s speech acquired
from the microphone is speech-recognized, and the transcribedtext is divided into words using morphological analysis andsent to the dialog management module. We used Mecab [23]as the morphological analysis engine.
C. Dialog management module
If a neural network-based response generator is used, theagent’s response cannot be controlled. To evaluate customer
service properly, it is desirable to use a dialog management
module that can control the virtual agents’ responses. Furthermore, we would like to evaluate whether the trainee canrespond adequately to nasty behavior by the customer agent.
Consequently, we use the simple and traditional rule-based
dialog management approach in this study.
Management of the customer service flow and of the dialog
status is performed using the state transitions shown in Fig.
2. The dialog state is defined in the training dialog scenario
described in Section 2.4 below.
For each dialog state, keywords that are important incustomer service are set in the dialog scenario file, alongwith the dialog state transitions according to the customer
service content (speech content) of the trainee. As the dialog
management module loads a training dialog scenario file,state transitions depending on the training dialog scenario
are expanded in the module. At the same time, any delay inresponse time is suppressed by synthesizing the lines uttered404

Proceedings, APSIPA Annual Summit and Conference 2020

7-10 December 2020, Auckland, New ZealanddialogscenarioloadYour bill is
218 Yen..

transcription

ASR

system control module

dialog management module
responseof the agent

management of
dialog stateGUI agent’s line advice explanation of the situation

Trainee(staff of convenience store)
Here you are.

Customer(virtual agent)speech ID
synthesized speech
control module

Fig. 1. Outline of the spoken dialog training system for customer service improvement.
TABLE IA SIMPLE EXAMPLE OF TRAINING SCENARIO .
Current
state
greeting
checkout
checkout

Next
state
checkout
receipt
correction

Transition
trigger
STEP1
STEP1
STEP0

correction
receipt
handoverthanksreceipt
handoverthanksfinish

STEP2
STEP3
STEP2
STEP1

Keywords

Lines of the customer“Welcome”“218 Yen”“218 Yen”“I’ll take this.”“Here you are.”“Sorry, the price iswrong.”“OK”“OK”“Thanks”—“Sorry”, “218 Yen”
“Take”, “218 Yen”, “Exactly”“Here”, “Yours”“Thank you”

by the customer agent when the scenario is loaded into the
management module. For example, when the training dialog
scenario data shown in Table I is loaded into the dialog
management module, the state transitions shown in Fig. 2 aredeveloped in the module.
D. Dialog scenario
A training dialog scenario must be created for each training topic (e.g., checkout at the counter of the convenience
store). It consists of six items: “current state”, “next state”,“transition trigger”, “keywords”, “lines of the customer agent”,
and “situation description and advice to the trainee.” Table I
shows a simple example of a dialog scenario for customer
service training at the checkout counter in a convenience
store. From this scenario, the state transitions shown in Fig.
2 are generated automatically. By preparing a variety of
training dialog scenario data, we can provide various customer
service training options, such as complaint handling in various
industries such as convenience stores and restaurants.
E. Dialog management based on scenarios
Management of the transition of the interactive state isbased on the keywords contained in the speech recognition
results of the trainee’s voice. This is essentially the same as
in a traditional spoken dialog system. The next destinationof the dialog state transition is determined by how many
keywords are included in the scenario. However, since our

Situation description andadvice to the trainee
Tell the total price.
Receive money.
Apologize and tell the customer
the right amount.
Receive money.
Pass a stuff to the customer.
Say “Thank you.”—system is aimed at customer service training, keyword-basedstate transitions alone are not sufficient, because this would
make the scenario monotonous and would degrade the effectof the training. Therefore, the scenario designer is enabled to
set up transition triggers for the creation of various training
patterns.
In our dialog system, we prepared seven transition triggers1 .
One is the “STEP” trigger, which is shown in Table I. This is a
trigger that changes the destination of the transition depending
on how many of the keywords in the scenario match. For
example, if the “STEP1” trigger is set in the state transition
destination, the transition to the destination will occur when
the trigger matches one of the words in the keyword set. A
trainee is at the “greeting” state in Table I when the virtual
agent comes to the checkout counter to play, and the trainee
is asked to say “Welcome” to the customer. If the traineesays “Welcome” correctly, the dialog state will move to the“checkpoint” state depending on the “STEP1” trigger.
F. Graphical User Interface
Fig. 3 shows the GUI of our spoken dialog training systemfor customer service improvement. The background image
and the agent can be changed arbitrarily. The text of the
speech recognition results after the morphological analysis isdisplayed in the upper left corner. This enables us to confirm
1 These included “RAND”, “RUN”, “STEP”, “CHOOSE”, “MULTI”, and“LOOP.” A transition trigger was used to introduce branching to a scenario.

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Proceedings, APSIPA Annual Summit and Conference 2020

greeting

STEP1

Transition trigger

checkout

STEP1

receipt

7-10 December 2020, Auckland, New ZealandSTEP3

handoverSTEP2

thanksSTEP1

finish

STEP2

STEP0

* if no keywords is correctly recognized,
the state will go to the same (self-loop)

correction

Fig. 2. Example of state transitions from the dialog scenario shown in Table I.

TABLE IIF IVE TRAINING DIALOG SCENARIOS USED IN THE EXPERIMENT.
Scene
Scenario
Convenience 1. Checkout operation with guidestore
2. Checkout operation without guide
3. Customer’s complaint for buying cigarettes
Restaurant
4. Order from a customer
5. Customer’s complaint on the orderconvenience store
restaurant

ASR result after morphological analysis“The bill is 218 Yen.”

Buttons for
Scenariochange3D model of thevirtual agent
utterance of the customer

A. Experimental setup
Description of the current situation
and advice to trainee

Fig. 3. Graphical user interface of the spoken dialog module.

Fig. 4. Scene of the experiment.

how the trainee’s speech is heard by the virtual customer agent.
The trainees’ speech, “The total bill is 218 Yen,” is shown in
Fig. 3. The buttons in the upper right corner are used forchanging the scenario. This allows the trainer to select the
prepared training dialog scenario and conduct customer service
training using the selected scenario.

III. E XPERIMENTIn this section, we describe an experimental test of ourdeveloped spoken dialog training system.

The subjects included 19 undergraduate and graduate students. Most of the subjects had had the experience of working
at a convenience store and/or restaurant. Consequently, theeffectiveness of customer service training can be evaluated.
Each subject wore a microphone and stood in front of amonitor, as shown in Fig. 4. The microphone used in thisexperiment was a head-worn vocal microphone with supercardioid directional characteristics, which provides high soundinsulation from environmental noises. This system is capableof keyboard-based text input; however, none of the subjects
did use text input, but only speech input.
In this experiment, subjects evaluated our dialog training
system on the seven items:
1) Visibility of the GUI,
2) Ease of use of the system,
3) Ease of use of the interactive training,4) Smoothness of the speech interaction,5) Naturalness of the training dialog,
6) Reproducibility of the real-world environment, and
7) Understanding of customer service methods when thesystem is used.
All of the subjects rated each evaluation item on a 5-point scalefrom one to five. This five-point scale is the Mean Opinion
Score (MOS), where 1 represents poor and 5 represents
excellent.
We prepared five training dialog scenarios to be used in
the experiment, as shown in Table II. In the training dialog
scenario on convenience store customer service, the subjects
practiced what kind of customer service is required in any
given situation at the accounting counter using three sortsof situations. On the other hand, in the dialog scenarios onrestaurant service, the subjects practiced how to serve when
taking an order from a customer and how to serve the food. In
addition, the subjects practiced how to respond to complaints
on orders from a customer.

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Proceedings, APSIPA Annual Summit and Conference 2020

7-10 December 2020, Auckland, New Zealand

TABLE IIIS UMMARY OF THE AVERAGE RATING RESULTS FROM THE SUBJECTS .
Evaluation Items
Rating
1. Visibility of the GUI
4.3
2. Ease of use of the system
4.1
3. Ease of use of interactive training
3.4
4. Smoothness of dialog
3.0
5. Naturalness of dialog
3.6
6. Reproducibility
4.1
4.3
7. Understanding of customer service

B. Result and discussion
Table III summarizes the rating results of the seven evaluation items obtained by averaging all of the scores from thesubjects. “The visibility of use of the GUI” rated very high at
4.3 because of the simplicity of the user interface. The userinterface displays only the minimum necessary information.
Therefore, most subjects could focus on the virtual agent
playing a customer and the advice as a suggestion of customer
service. “Ease of use of the system” also received a high ratingof 4.1, because the customer service process could proceed
only through speech interaction, which is very close to theactual environment. Based on the high evaluation of the userinterface and the ease of use of the dialog system, we canconclude that our spoken dialog training system requires noadditional human instructors and is easy for one person to use.
However, the subjects scored 3.4 points for ease of use ofinteractive training, 3.0 points for smoothness of the spoken
dialog, and 3.6 points for naturalness of the training dialog,
all of which resulted in a score of fewer than four points. The
reason for the low usability of the interactive training is interpreted to be that the subjects often had to rephrase their words
repeatedly because the keywords were incorrectly extracted
owing to misrecognition by the ASR system. Although spoken
dialog is very convenient for customer service training, it mustalso be accompanied by high speech recognition accuracy. In
addition, the reason for the low evaluation of the smoothness ofthe dialog was that the response speed of the virtual customer
and the speed of speech recognition created subtle intervals
during interaction with the dialog system. This does not arise
in human interaction. The ease of use and smoothness of the
spoken dialog affected its naturalness.
Although some improvements are still required in our
dialog system, the reproducibility of the customer service andthe subjects’ understandings for customer service were rated
highly. In summary, the spoken dialog system for customer
service training might shows promise in practice as a replacement for existing training methods.

The experiments revealed that the system could be used forpractical training.
However, according to the experiment, our dialog systemneeds improvement because of the low scores on the ease ofuse and smoothness of the spoken dialog and the naturalnessof the training dialog. In particular, improvements in the ASR
system are required to improve the ease of use of speech
dialogs. Currently, we are using Microsoft’s ASR system,
which is easy to call in the Unity environment, but we areplanning use the Kaldi ASR toolkit, which can be customized
with a language model and dictionary. In addition, sinceKaldi runs on-premises on a local server, the speed of speech
recognition can be expected to be improved, and the agent’s
response time can be expected to be faster [24]. Replacing
the ASR system is aimed at improving the usability of thesystem further. In the training system, the manner of speaking[25] and politeness of wording [26] must also be assessed, andwe intend to develop such assessment modules. Moreover, we
will also incorporate emotional analysis based on the trainee’s
voice and face image as in the dialog system in [27]. We alsoplan to conduct a comparative experiment to evaluate what
is better and what improvements are needed between actual
human instruction and this system. Finally, in our system, we
adopted rule-based response generation, but a deep learningbased generator can be expected to provide more natural
responses [28]. The use of a deep learning-based response
generator such as the one in [29] will also be considered in
the future.
ACKNOWLEDGMENT
This work was supported by JSPS KAKENHI Grant Number 20H05558.

IV. C ONCLUSIONS AND FUTURE WORKIn this paper, we introduced our prototype dialog training
system for customer service improvement. Because of theneed for strict dialog state management in hospitality training
dialogs, we used simple rule-based dialog management. The
dialog scenarios were prepared by humans to enable subjects
to experience a variety of situations in the hospitality industry.

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R EFERENCES[1] K. Chopra, “Empirical study on role of customer service in deliveringsatisfaction at branded retail outlets in pune,” Procedia Economics andFinance, vol. 11, pp. 239 – 246, 2014.
[2] M. Holt, “The impact of customer service on customer satisfaction,”
https://smallbusiness.chron.com/impact-customer-service-customersatisfaction-2087.html, 5 2020.
[3] P. Gannon-Leary and M. McCarthy, Customer Care: A Training Manual
for Library Staff. Chandos Publishing, 2010.
[4] P. Jain, “On-the-job training: A key to human resource development,”
Library Management, vol. 20, pp. 283–294, 08 1999.
[5] X. Jin, Y. Bian, W. Geng, Y. Chen, K. Chu, H. Hu, J. Liu, Y. Shi, andC. Yang, “Developing an agent-based virtual interview training systemfor college students with high shyness level,” in 2019 IEEE Conference
on Virtual Reality and 3D User Interfaces (VR), 2019, pp. 998–999.
[6] J. V. Helvert, P. V. Rosmalen, D. Börner, V. Petukhova, and J. Alexandersson, “Observing, coaching and reflecting: A multi-modal natural
language-based dialogue system in a learning context,” in Proceedingsof the 11th International Conference on Intelligent Environments, 2015,
pp. 220–227.
[7] C. Wu, R. Socher, and C. Xiong, “Global-to-local memory pointernetworks for task-oriented dialogue,” CoRR, vol. abs/1901.04713, 2019.
[Online]. Available: http://arxiv.org/abs/1901.04713[8] S. Kim and R. E. Banchs, “R-cube: A dialogue agent for restaurant
recommendation and reservation,” in Signal and Information Processing
Association Annual Summit and Conference (APSIPA), 2014 AsiaPacific, 2014, pp. 1–6.
[9] M. S. Yakoub and S. A. Selouani, “Ontology-based framework for a
multi-domain spoken dialogue system,” Journal of Ambient Intelligenceand Humanized Computing, https://doi.org/10.1007/s12652-017-0625-y,2017.

Proceedings, APSIPA Annual Summit and Conference 2020

7-10 December 2020, Auckland, New Zealand[10] H. Kashioka, T. Misu, E. Mizukami, Y. Shiga, K. Kayama, C. Hori, and
H. Kawai, “Multimodal dialog system for kyoto sightseeing guide,” inProceedings of the 3rd Annual Summit and Conference (APSIPA ASC
2011), 2011, pp. 1–6.
[11] J. Bang, H. Noh, Y. Kim, and G. G. Lee, “Example-based chat-oriented
dialogue system with personalized long-term memory,” in 2015 International Conference on Big Data and Smart Computing (BIGCOMP),
2015, pp. 238–243.
[12] A. Inc, “Apple siri,” https://www.apple.com/siri/, 2020.
[13] H. Cuayáhuitl, S. Renals, O. Lemon, and H. Shimodaira, “Evaluationof a hierarchical reinforcement learning spoken dialogue system,” Computer Speech & Language, vol. 24, no. 2, pp. 395 – 429, 2010.
[14] X. Li, Y.-N. Chen, L. Li, J. Gao, and A. Celikyilmaz, “Investigationof language understanding impact for reinforcement learning based
dialogue systems,” 2017.
[15] S. Young, M. Gašić, B. Thomson, and J. D. Williams, “Pomdp-based
statistical spoken dialog systems: A review,” Proceedings of the IEEE,
vol. 101, no. 5, pp. 1160–1179, 2013.
[16] S. Constantin, Niehues, and A. Waibel, “An end-to-end goal-oriented
dialog system with a generative natural language response generation,”in 9th International Workshop on Spoken Dialogue System Technology,
2019, pp. 209–219.
[17] S. Lee, Q. Zhu, R. Takanobu, Z. Zhang, Y. Zhang, X. Li, J. Li, B. Peng,
X. Li, M. Huang, and J. Gao, “ConvLab: Multi-domain end-to-end
dialog system platform,” in Proceedings of the 57th Annual Meeting of
the Association for Computational Linguistics: System Demonstrations,Jul. 2019, pp. 64–69.
[18] N. Braunschweiler and A. Papangelis, “Comparison of an end-to-end
trainable dialogue system with a modular statistical dialogue system,”in Proceedings of INTERSPEECH2018, 2018, pp. 576–580.
[19] X. Yang, Y. Chen, D. Hakkani-Tür, P. Crook, X. Li, J. Gao, and
L. Deng, “End-to-end joint learning of natural language understanding
and dialogue manager,” in 2017 IEEE International Conference on
Acoustics, Speech and Signal Processing (ICASSP), 2017, pp. 5690–
5694.
[20] U. Technologies, “Unity,” https://unity.com, 2020.
[21] T.
Yarkoni,“Pytransitions/transitions,”
https://github.com/pytransitions/transitions, 5 2020.
[22] OpenJTalk, “The Japanese TTS system ”Open JTalk”,” http://openjtalk.sourceforge.net, 2020.
[23] “Mecab: Yet another part-of-speech and morphological analyzer,”
http://taku910.github.io/mecab/, 2020.
[24] C.-S. Leow, T. Hayakawa, H. Nishizaki, and N. Kitaoka, “Developmentof a low-latency and real-time automatic speech recognition system,” in2020 IEEE 9th Global Conference on Consumer Electronics, 2020, p.
Accepted.
[25] H. Deng, Y. Lin, T. Utsuro, A. Kobayashi, H. Nishizaki, and J. Hoshino,“Automatic fluency evaluation of spontaneous speech using disfluencybased features,” in Proc. of ICASSP2020, 2020, pp. 9239–9243.
[26] S. Iida, T. Utsuro, H. Nisizaki, and J. Hoshino, “Scenario-based customer
service vr training system with honorific exercise,” in Proc. of the 6th International Conference on Biomedical and Bioinformatics Engineering,
2019, pp. 158–162.
[27] Y. Matsuyama, A. Bhardwaj, R. Zhao, O. J. Romero, S. A. Akoju, and
J. Cassell, “Socially-aware animated intelligent personal assistant agent,”in Proc. of the 17th SIGDIAL, 2016, p. 4.
[28] Q. Yang, Z. He, Z. Zhan, R. Li, Y. Lee, Y. Zhang, and C. Hu, “End-toend personalized humorous response generation in untrimmed multi-role
dialogue system,” IEEE Access, vol. 7, pp. 94 059–94 071, 2019.
[29] A. Hatua, T. T. Nguyen, and A. H. Sung, “Dialogue generation using
self-attention generative adversarial network,” in 2019 IEEE International Conference on Conversational Data Knowledge Engineering(CDKE), 2019, pp. 33–38.

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