A STUDY OF SOCIAL-AFFECTIVE COMMUNICATION:
AUTOMATIC PREDICTION OF EMOTION TRIGGERS AND RESPONSESIN TELEVISION TALK SHOWS
Nurul Lubis, Sakriani Sakti, Graham Neubig, Koichiro Yoshino, Tomoki Toda, Satoshi Nakamura
Augmented Human Communication Laboratory
Graduate School of Information Science
Nara Institute of Science and Technology{nurul.lubis.na4, ssakti, neubig, koichiro, tomoki, s-nakamura}@is.naist.jp

ABSTRACT
Advancements in spoken language technologies have allowed
users to interact with computers in an increasingly naturalmanner. However, most conversational agents or dialoguesystems are yet to consider emotional awareness in interaction. To consider emotion in these situations, social-affective
knowledge in conversational agents is essential. In this paper,
we present a study of the social-affective process in naturalconversation from television talk shows. We analyze occurrences of emotion (emotional responses), and the events thatelicit them (emotional triggers). We then utilize our analysis for prediction to model the ability of a dialogue system
to decide an action and response in an affective interaction.
This knowledge has great potential to incorporate emotion
into human-computer interaction. Experiments in two languages, English and Indonesian, show that automatic prediction performance surpasses random guessing accuracy.
Index Terms: emotion, affective communication, dialoguesystem1. INTRODUCTION
The study of social-affective communication is concernedwith the role of emotion in human interaction. Social affective
aspects of communication deeply enrich human interaction,highly affecting the way a speaker or listener behaves and responds. It is natural for humans to reflect their emotion incommunication and be affected by their conversational counterpart. However, this is yet to be completely replicated inhuman-computer interaction (HCI).
The most widely researched sub-area of social-affective
communication is emotion recognition, in which a computerattempts to recognize the emotion of a speaker from data inseveral modalities [1] [2] [3]. A number of emotion challenges have been held in the recent past, such as those at INTERSPEECH [4] [5] [6] as well as the Audio Visual Emotion Challenge (AVEC) [7] [8]. There has also been somework on emotion simulation in dialogue systems [9], where978-1-4799-7291-3/15/$31.00 ©2015 IEEE777

the computer attempts to give the impression that it is feelinga particular emotion.
In addition to these more traditional works on recognitionand simulation, there has recently been an increasing interest
in emotion elicitation, or emotional triggers, studying whatcauses emotion in the first place. Because emotion plays a
two-way role in social-affective communication, knowing thereason behind displayed emotion is crucial in imitating conversations between humans. A recent study by Hasegawa etal. [10] addresses this issue by predicting and eliciting emotion in online conversation. We have followed up this study[11] by performing a similar task on the emotionally colored
conversation between humans and simulated agents [12]. We
have also performed a study expanding the largely Englishcentered previous work to Indonesian [13].
In this paper, we extend upon previous user-centeredworks that tried to predict and alter user’s emotion. Instead,
we try to predict a person’s emotional reaction in a socialaffective conversation by examining not only the person having the reaction, but also their conversational partner. To accomplish this, we analyze and observe how emotion fluctuatesand how it is connected to actions taken in discourse. This
analysis is intended to provide the knowledge to perform prediction of emotional triggers and responses. The prediction
is aimed to accommodate the abilities required of a conversational agent or dialogue system to be emotionally aware: (1)to be able to decide an emotion triggering action, and (2) to beable to predict the appropriate response to an emotion trigger.
Two languages are examined: English and Indonesian.
2. SOCIAL AFFECTIVE COMMUNICATIONIn this study, we attempt to analyze social-affective aspectsof an interaction and utilize them for prediction. To achieve
this goal, we need a set of data that represents social-affective
interaction. From this data, we would like to observe two major aspects of the conversation: (1) the emotional aspect, howemotion fluctuates and affects the conversation, and (2) the

ASRU 2015

social aspect, concerning the conversational give and take between speakers. In this section, we describe the definition ofproperties of conversation to allow the aforementioned observations.
Defining and structuring emotion is essential in observing
and analyzing its occurrence in conversation. We define theemotion scope based on the circumplex model of affect [14].
Two dimensions of emotion are defined: valence and arousal.
Valence measures the positivity or negativity of emotion; e.g.
the feeling of joy is indicated by positive valence while fearis negative. On the other hand, arousal measures the activityof emotion; e.g. depression is low in arousal (passive), whilerage is high (active).
From the valence-arousal scale, we derive 4 common
emotion terms: happiness, anger, sadness, and contentment.
In correspondence to the valence-arousal dimensions, happiness is positive-active, anger is negative-active, sadness isnegative-passive, and contentment is positive passive. Figure
1 illustrates these emotional dimensions.

Table 1: Dialogue acts
id
stat
opi
back
Qyno
Qopn
Qwh
Qbck
conf
deny

Dialogue Act
Statement
OpinionBackchannel
Yes-No Question
Open Question
Wh Question
Backchannel Question
Agree/Confirm
Disagree/Deny

Dialogue Act
Repeat Phrase
Acknowledgement
Thanking
Appreciation
Apology
Hedge
Directive
Abandoned

id
rept
ack
thnk
apcr
aplg
hdg
drct
abdn

The following sections will demonstrate how these properties help us in observing social-affective aspects in a conversation.
3. TASK DESCRIPTION….

High arousal

Anger
Negativevalence

SadnessA

Happiness

PositivevalenceEnglishconversation

Indonesianconversation

Contentment

B

Tell me about your experience in themountains.
You climb mountains?

C

Yeah, um one time I got lost for days.

A

That must be frightening!

C

Yeah, but fortunately I survived.

B

Where was that?
…

Low arousal

Fig. 1: Emotion classes and dimensions
On the other hand, to analyze the social aspect, it is necessary to observe the relationship of the utterances in a dialogue.
To do this, we define a set of dialogue acts adapted from [15]
to describe the structure of discourse. We reduce the original set of dialogue acts from 42 to 17 by grouping together
similar acts, such as Yes-No-Question and Declarative YesNo-Question. The 17 dialogue act labels are given in Table
1.
Another thing to pay attention to is that in natural conversation, interaction can be unordered and disconnected from
one turn to the other. For example, an utterance can be abandoned, a speaker can get ignored, and the topic might drastically shift. Therefore, to properly analyze the fluctuation ofemotion in a conversation, it is necessary to ensure that theobserved sequences of conversation are in response to eachother. To do this, we group consecutive sequences of conversation into a unit called a tri-turn [16]. Three consecutive
sequences of speech in a conversation is considered a tri-turnwhen the second sequence is in response to the first, and thethird is in response to the second.

778

Tri-turn filtering

Tri-turn sequences

Trigger and response
analysis

Trigger and response
prediction

Fig. 2: Overview
Figure 2 presents the overview of our experiment. Triturns will be the basis of observation and analysis in thisstudy. On each tri-turn, we analyze the change of emotion
from the first sequence to the third, and correlate it to the second sequence that acts as the trigger. We categorize changesof emotional state into 7 events, described in Table 2.
To examine the connection between dialogue acts andchanges of emotion occurring in conversation, we adapt theTerm Frequency-Inverse Document Frequency (TF-IDF) formula to measure the importance of each dialogue act in triggering a certain emotion event. This formula is written as (1)tf idf (d, t, T ) = f (d, t) × log{t ∈ T },1 + {t ∈ T : d ∈ t}(1)

where d is the dialogue act, t is the emotion event, and T isthe collection of events. f (d, t) denotes the raw frequency

of d in t. This score is calculated for each dialogue act oneach emotion event. This score can inform us if a particulardialogue act is characteristic of a particular emotion event.
Following the analysis, we perform automatic predictionof emotional triggers and responses. Two experiments are carried out. First, given the two sequences of interaction, we try
to predict the emotional response that will occur. Second,
given an emotional response, we try to predict characteristicsof the trigger of that response. In our experiments, we use
90% of our data for training and test the model with the remaining 10%.

4.1. Annotation procedure
In annotating the corpus, we bear in mind that language andculture affect how emotion is perceived and expressed in aninteraction. We carefully select 6 annotators for the task, 3 foreach language. Every annotator is required to be (1) a native
speaker of the language used in the show, and (2) knowledgeable of the culture in the interaction of the show. To ensure
consistency, we have each annotator annotate the full corpus.

Briefing

Preliminary
Annotation

Revision

Annotation

4. DATA CONSTRUCTION

Fig. 3: Overview of annotation procedure

In this paper, we collect our data from television talk shows,which contain real conversation with natural emotion occurrences. In television talk shows, the participants converse
naturally about various emotion-provoking topics. The host
directs the conversation to ensure that the discourse is structured as well as interesting. Due to these facts, this data isideal for social-affective analysis. Furthermore, television
talk shows provide clean speech recordings with distinguishable dialogue turns, as well as high quality speech data.
We expand the Indonesian Emotional Speech Corpus(IDESC) [17] with English conversational data from various
television talk shows. Previously, we have collected Indonesian conversational data consisting of 1 hour, 34 minutes, and49.7 seconds of speech. In this paper, we include English
conversational data consisting of 1 hour, 2 minutes, and 19
seconds of speech from various television talk shows.
In both languages, we select three episodes from different
kinds of talk shows to cover a broader range of emotion. In
Indonesian, we select three episodes with discussion on politics, entertainment, and humanities. In English, we collect thedata from three episodes of two of the most popular American television talk shows with discussion on entertainment,
life experiences, and family struggles.

Figure 3 gives an overview of the annotation procedure.
Before annotating the corpus, the annotators are briefed andgiven a document of guidelines to get a clearer picture of
the task and its goal. The annotators proceed with preliminary annotation by working on a small subset of the corpus.
This preliminary annotation lets the annotators get familiar
with the task and confirms their annotation quality. After wescreen the result, they are asked to revise inconsistencies withthe guidelines if there are any. We perform the same screenand-revise process on the full corpus annotation to achieve atenable result.

Table 2: Types of emotion eventsTypeemo drop

Description
positive emotion (happiness or contentment)changes to negative emotion (anger or sadness)emo raise negative emotion (anger or sadness) changes
to positive emotion (happiness or contentment)
act drop level of activation changes from high to low
act raise level of activation changes from low to highval drop valence changes from positive to negative
val raise valence changes from negative to positive
cons
no significant change is observed779

4.2. Labels
With the axes in Figure 1, two sets of emotion labels are defined to allow observation from different perspectives. These
sets are given in Table 3. The first emotion label set is foremotion dimension, consisting of the level of arousal and activation. The value of each dimension can be as low as -3 andas high as 3.
The second set is for emotion class; happiness, anger, sadness, and contentment. Instead of choosing which emotion ispresent, for each class, the annotators are instructed to rate
its degree of presence. This rate ranges from 0 to 3, with 0
meaning that the emotion is not present and 3 meaning thatthe emotion is intensely present. This annotation scheme allows the observation of mixed emotion in speech.
To analyze the annotation consistency, we calculate mean
Pearson’s correlation coefficients r of the three annotators foreach language, as shown in Figure 4. Moderate correlation isobserved on all emotion labels except for contentment, which
has weak correlation.
Dialogue act labels are defined according to Table 1. Annotators are asked to choose the proper dialogue act label oneach dialogue turn. To analyze the consistency of dialogueannotation, we calculate Fleiss’ kappa κ of the three annotators’ results. Indonesian dialogue act annotation has a κ of
0.54 and English 0.45.

emotion raisevalence raise
0.14

0.50

0.12

0.40

0.10

0.30

0.08

emotion raisevalence raise

We perform our analysis on the pre-processed data by correlating dialogue acts and the occuring emotion events. Thissection presents the result of the analysis and a potential application for emotional awareness in HCI.
5.1. Emotion dynamics and dialogue acts

EnglishIndonesian

To analyze the fluctuation of emotion in a conversation,
on each tri-turn collected from our corpus, we observe thechange of emotion from the first sequence to the third (emotional response), and correlate it to the second sequence (emotional trigger).

10%

opi
ack

20%

back
thnk

30%

40%

50%

60%

Qyno
aprc

Qopn
aplg

Qwh
hdg

70%

Qbck
drct

abdn

hdg

drct

aplg

rpt

ack

conf

deny

Qwh

thnk
aprc

arousal raise

0.04

80%

90%

conf
abdn

100%

deny

Fig. 5: Dialogue act frequency on triggers of all emotion
eventsFirst, we take a look at all the emotional triggers, regard-

780

abdn

drct

hdg

aplg

thnk
aprc

rpt

ack

conf

deny

Qbck

Qwh

0.02

Qopn

5. ANALYSIS OF EMOTION DYNAMICS

0%

arousal drop
ignore0.06

Emotion Class
Happiness
Anger
Sadness
Contentment

0.00

stat
rept

emotion drop
valence drop

0.08

TF-IDF SCORE

id
hap
ang
sad
con

DIALOGUE ACT(a) English

Table 3: Emotion label sets
Emotion Dimension
ArousalValence

Qbck

0.00

Fig. 4: Correlation coefficients of the emotion annotations

id
aro
val

arousal raise

0.02

Qopn

val
0.64
0.33

back

aro
0.30
0.37

back

con
0.26
0.14

Qyno

sad
0.55
0.54

stat_neu
opi

ang
0.51
0.54

stat_posEnglishIndonesian

hap
0.42
0.48

stat_neg

0.00

arousal drop
ignore

0.04

Qyno

0.10

emotion drop
valence drop

0.06

stat_neu
opi

0.20

stat_pos

TF-IDF SCORE

0.60

stat_neg

0.70

DIALOGUE ACT(b) IndonesianFig. 6: Dialogue acts scores for all emotion events
less of the emotion event they elicit, and see the frequency of
dialogue acts. Figure 5 visualize the percentage of dialogue
act frequencies for both languages. Statement has the highest
frequency for both languages.
Second, we look into details for each emotion event. For
each event, we calculate the TF-IDF score of each dialogue
act using Equation (1). As the frequency of stat is high for
all types of change, we separate statements according to theiremotion, positive, negative, and neutral, and calculate theirscores accordingly.
Figure 6 visualizes the TF-IDF scores of the dialogue acts,
where a point in the graph corresponds to the TF-IDF score
for a dialogue act on an emotion event. A large number ofoverlapping dots for a dialogue act means its TF-IDF score issimilar for all emotion events. When this is observed, we canconclude that the dialogue act does not characterize a particular emotion event.
Comparison of Figures 6a and 6b tells us that the emotion events have different characteristics between the two languages when viewed from the action that triggers them. In
English, the dots overlap one another with only slight differences, signaling that dialogue acts weakly characterize emotion events. On the other hand, in Indonesian, fewer overlapping dots are observed. This means, in English, emotion
events are not attributed to the act taken in discourse, as opposed to Indonesian.

IndonesianDifferent tendencies can be observed from Figure 8. In
English, the emotion triggering statements have an even distribution of positive and negative emotion. Statements withpositive emotion spread to the upper right and lower left quadrants, meaning they cause an increase or decrease of both valence and arousal at the same time. On the other hand, statements with negative emotion give opposing effects to valence
and arousal.
Unlike in English, in Indonesian, negative emotion dominated the emotion triggering statements. However, the few
statements that have positive emotion seem to have a bigger
effect compared to the negative ones.

English0.12
0.10

TF-IDF SCORE

0.08
0.06
0.04
0.02
0.00

DIALOGUE ACT

Fig. 7: Average scores of dialogue acts in English
and Indonesian3change in arousal

2

Figure 7 shows the TF-IDF score of the dialogue acts
in English and Indonesian averaged over all emotion events.
From this figure, we can identify significant dialogue acts intriggering emotion events. In English, the top five dialogue
acts are stat, back, Qyno, conf, and ack. In Indonesian,
the top five dialogue acts are stat, Qyno, back, conf, andQwh.
This finding suggests that showing interest in the conversation through questions and backchannels is a way to emotionally engage with the counterpart. Furthermore, providing
new information in conversation can also elicit an emotional
response from the counterpart. Table 4 includes an example
of conversation taken from the corpus that demonstrates thisfinding. Looking at the level of valence and arousal, we cannotice an increase for both the host and the guest.

1
0
1
2
3
2.0

positive
negative
1.5

1.0

0.5
0.0
0.5
change in valence

1.0

1.5

2.0

(a) English
2.5
2.0

Table 4: Example of conversation

Host
Guest
Host
Guest

Transcription
Well I still have a lot ofclothes in my closet I reallyshouldn’t have
Yeah
But—yeah
Why?
Just [inaudible] I want to sayjust in case but I don’t think
so ’cause I really think I got
it conquered this timeact
aro
stat 0

back
conf
Qwh
opi

-1
1
0
2

val
-1

0
1
1
2

change in arousal

Speaker
Guest

1.5
1.0
0.5
0.0
0.5
1.0

positive
negative

1.5
2.0
2.5

2.0

1.5

1.0

0.5 0.0
0.5change in valence

1.0

1.5

2.0

(b) IndonesianFig. 8: Emotion of statements correlated to the emotion
change it triggers

5.2. Emotion dynamics on statements
Statements make up a large part of human conversation.
Therefore, we attempt to take a closer look at statements asemotional triggers by grouping them according to the emotion of the statement and plot them according to the emotional
response. This distribution is shown in Figure 8.

781

6. AUTOMATIC PREDICTION OF EMOTIONALTRIGGERS AND RESPONSES
In considering emotion, two of the important abilities of conversational agents or dialogue systems are (1) to be able todecide an emotion triggering action, and (2) to be able to predict the appropriate response to an emotion trigger. As themain goal of this study is incorporating emotion in HCI, we
attempt to model the result of our analysis, allowing for automatic prediction to accommodate these abilities.
To gather information from the speech, we extract acoustic features as defined in the INTERSPEECH 2009 emotion
chalenge [4] using the openSMILE feature extractor [18].
This feature set includes the most common yet promising feature types and functionals covering prosodic, spectral, andvoice quality features.
On each tri-turn, we stack the features of the two corresponding tri-turn sequences to gather information of the context. To balance the number of instances and features, we
perform correlation-based feature extraction [19] and linear
discriminant analysis of our feature set. After reducing thedimension, we train a deep neural network classifier using
Theano and the PDNN toolkit [20].
6.1. Automatic prediction of dialogue act of triggers
For dialogue act prediction, given the first and last sequenceof a tri-turn as an emotional response, we try to automatically
predict what action takes place as the trigger. We utilize theacoustic information, emotion annotation, and dialogue actof the first and last sequence as classification features. The
prediction target is 17 dialogue acts presented in Table 1. This
calculates to a chance rate of 5.88%.
For the trigger prediction task, we achieve an accuracy of
53.97% for English, and 31.58% for Indonesian. The lower
performance for Indonesian is suspected to be the implicationof the dialogue act analysis in Section 5. In Indonesian, different emotion events can be triggered by different actions.
Learning of these more complex patterns is likely to require alarger amount of data than currently present.
On the other hand, for English, triggers of a certain emotion event aren’t strongly characterized by the dialogue act. In
other words, the occuring emotion event only weakly affects
the dialogue act, while other features such as acoustic featuresand dialogue act helps make the prediction.
6.2. Automatic prediction of emotional responses
Next, to simulate ability to understand social-affective dynamics in natural interaction, we attempt to predict the emotional response given an interaction of two speakers in a triturn. From tri-turn pairs in our corpus, we utilize the acoustic
information, emotion annotation, and dialogue act of the firstand second sequence as classification features.
We attempt to predict whether emotion events defined inTable 2 occur. As the emotion events can occur simultaneously, we examine the emotion class, valence level, andarousal level separately and try to predict whether cons,raise, or drop occurs. With three classes for prediction,
we have a chance rate of 33.33%.

782

60.00%

40.00%

20.00%

0.00%
EnglishIndonesianemo
44.44%
45.62%

aro
44.45%
45.62%

val
50.80%
54.39%

Fig. 9: Performance of response prediction
Figure 9 presents our response prediction accuracy. For
both languages, the prediction regarding valence has the highest performance, followed by arousal and emotion class. This
could mean that valence and its fluctuations are expressed inspeech more than is the case for arousal.
The suboptimal performance is likely due to the limited
amount of data used in this study. Inherently, there are numerous factors that leads to a change of emotion in a conversation. This means that to properly recognize patterns forsuch events, a large number of features are required. It is
likely that if we could prepare more data in the future, theaccuracy will increase significantly.
7. CONCLUSIONSIn this paper, we presented a study on social-affective communication for automatic prediction of emotional triggers
and responses. We examine natural conversations in English
and Indonesian and analyze the emotion events that occurred
within. Each language shows different tendencies and characteristics of emotional responses and triggers, suggesting thatemotion events are language dependent. We look forward toaffirm this finding, especially for English and Indonesian, on
a larger amount of data.
In providing users the most natural HCI, emotion is an
aspect that should not be overlooked. Our experiment on
automatic prediction offers an approach in equipping conversational agents and dialogue systems with social-affective
awareness. In future studies, we hope to include more modalities of interaction in observing the dynamics of emotion ininteraction, such as textual and visual features. We also hope
to incorporate this information directly into a dialogue system.

Acknowledgment
Part of this work was supported by JSPS KAKENHI Grant
Number 24240032 and 26870371, as well as by a joint research project with Yanmar Co., Ltd.

8. REFERENCES[1] W. Wang, G. Athanasopoulos, G. Patsis, V. Enescu, and
H. Sahli, “Real-time emotion recognition from natural
bodily expressions in child-robot interaction,” in Computer Vision-ECCV 2014 Workshops. Springer, 2014,
pp. 424–435.
[2] P. C. Petrantonakis and J. Leontios, “EEG-based emotion recognition using advanced signal processing techniques,” Emotion Recognition: A Pattern Analysis Approach, pp. 269–293, 2014.
[3] K. R. Anne, S. Kuchibhotla, and H. D. Vankayalapati,“Emotion recognition using spectral features,” in Acoustic Modeling for Emotion Recognition. Springer, 2015,
pp. 17–26.
[4] B. Schuller, S. Steidl, and A. Batliner, “The INTERSPEECH 2009 emotion challenge.” in INTERSPEECH,
vol. 2009, 2009, pp. 312–315.
[5] B. Schuller, S. Steidl, A. Batliner, F. Burkhardt, L. Devillers, C. A. Müller, and S. S. Narayanan, “The INTERSPEECH 2010 paralinguistic challenge.” in INTERSPEECH, 2010, pp. 2794–2797.
[6] B. Schuller, S. Steidl, A. Batliner, A. Vinciarelli,
K. Scherer, F. Ringeval, M. Chetouani, F. Weninger,
F. Eyben, E. Marchi et al., “The INTERSPEECH 2013
computational paralinguistics challenge: social signals,
conflict, emotion, autism,” 2013.
[7] B. Schuller, M. Valstar, F. Eyben, G. McKeown,
R. Cowie, and M. Pantic, “AVEC 2011–the first international audio/visual emotion challenge,” in Affective
Computing and Intelligent Interaction. Springer, 2011,
pp. 415–424.
[8] M. Valstar, B. Schuller, K. Smith, T. Almaev, F. Eyben,
J. Krajewski, R. Cowie, and M. Pantic, “AVEC 2014:
3d dimensional affect and depression recognition challenge,” in Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge. ACM, 2014,
pp. 3–10.
[9] C. Becker, S. Kopp, and I. Wachsmuth, “Simulating
the emotion dynamics of a multimodal conversational
agent,” in Affective Dialogue Systems. Springer, 2004,
pp. 154–165.
[10] T. Hasegawa, N. Kaji, N. Yoshinaga, and M. Toyoda,“Predicting and eliciting addressee’s emotion in online
dialogue.” in ACL (1), 2013, pp. 964–972.
[11] N. Lubis, S. Sakti, G. Neubig, T. Toda, A. Purwarianti, and S. Nakamura, “Emotion and its triggers in human spoken dialogue: Recognition and analysis,” Proc
IWSDS, 2014.

783[12] G. McKeown, M. Valstar, R. Cowie, M. Pantic, and
M. Schroder, “The SEMAINE database: Annotatedmultimodal records of emotionally colored conversations between a person and a limited agent,” Affective
Computing, IEEE Transactions on, vol. 3, no. 1, pp. 5–
17, 2012.
[13] N. Lubis, D. Lestari, S. Sakti, A. Purwarianti, and
S. Nakamura, “Emotion recognition on Indonesian television talk shows,” in Proc IEEE Spoken Language
Technology, 2014.
[14] J. A. Russell, “A circumplex model of affect.” Journalof personality and social psychology, vol. 39, no. 6, p.
1161, 1980.
[15] A. Stolcke, K. Ries, N. Coccaro, E. Shriberg, R. Bates,
D. Jurafsky, P. Taylor, R. Martin, C. Van Ess-Dykema,
and M. Meteer, “Dialogue act modeling for automatic
tagging and recognition of conversational speech,”
Computational linguistics, vol. 26, no. 3, pp. 339–373,
2000.
[16] N. Lasguido, S. Sakti, G. Neubig, T. Tomoki, and
S. Nakamura, “Utilizing human-to-human conversation
examples for a multi domain chat-oriented dialog system,” IEICE TRANSACTIONS on Information and Systems, vol. 97, no. 6, pp. 1497–1505, 2014.
[17] N. Lubis, D. Lestari, S. Sakti, A. Purwarianti, and
S. Nakamura, “Construction and analysis of Indonesianemotional speech corpus,” in Proc Oriental COCOSDA,
2014.
[18] F. Eyben, M. Wöllmer, and B. Schuller, “OPENsmile:
the munich versatile and fast open-source audio feature
extractor,” in Proceedings of the international conference on Multimedia. ACM, 2010, pp. 1459–1462.
[19] M. A. Hall, “Correlation-based feature selection for machine learning,” Ph.D. dissertation, The University ofWaikato, 1999.
[20] J. Bergstra, F. Bastien, O. Breuleux, P. Lamblin, R. Pascanu, O. Delalleau, G. Desjardins, D. Warde-Farley,
I. Goodfellow, A. Bergeron et al., “Theano: Deep learning on GPUs with python,” in NIPS 2011, BigLearning
Workshop, Granada, Spain, 2011.