Multimodal Dataset of Social Skills Training in Natural
Conversational Setting
Takeshi Saga

NAIST
Ikoma, Nara, Japansaga.takeshi.sn0@is.naist.jp

Yasuhiro Matsuda

Hiroki Tanaka

Hidemi Iwasaka

NAIST
Ikoma, Nara, Japan
hiroki-tan@is.naist.jp

Tsubasa Morimoto

Nara Medical University
Nara, Japan

Mitsuhiro Uratani

Nara Medical University
Nara, Japan

Nara Medical University
Nara, Japan

Nara Medical University
Nara, Japan

Kosuke Okazaki

Yuichiro Fujimoto

Satoshi Nakamura

Nara Medical University
Nara, JapanNAIST
Ikoma, Nara, Japanyfujimoto@is.naist.jp

NAIST
Ikoma, Nara, Japans-nakamura@is.naist.jp

ABSTRACTKEYWORDS

Social Skills Training (SST) is commonly used in psychiatric rehabilitation programs to improve social skills. It is especially effective
for people who have social difficulties related to mental illnesses ordevelopmental difficulties. Previous studies revealed several communication characteristics in Schizophrenia and Autism Spectrum
Disorder. However, a few pieces of research have been conducted innatural conversational environments with computational features
since automatic capture and analysis are difficult in natural settings. Even if the natural data collection is difficult, the data clearly
have much better potential to identify the real communication
characteristics of people with mental difficulties and the interaction differences between participants and trainers. Therefore, wecollected a one-on-one SST multimodal dataset to investigate andautomatically capture natural characteristics expressed by people
who suffer from such mental difficulties as Schizophrenia or Autism
Spectrum Disorder. To validate the potential of the dataset, using
partially annotated data, we trained a classifier for Schizophrenia
and healthy control with audio-visual features. We achieved over
85% accuracy, precision, recall, and f1-score in the classification
task using only natural interaction data, instead of data captured
in the specific tasks designed for clinical assessments.

Social Skills Training, Schizophrenia, Autism Spectrum Disorder,
Audio-visual featuresCCS CONCEPTS• Human-centered computing → Empirical studies in interaction design; • Applied computing → Health care information
systems.
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ACM ISBN 978-1-4503-8471-1/21/10. . . $15.00
https://doi.org/10.1145/3461615.3485425

ACM Reference Format:
Takeshi Saga, Hiroki Tanaka, Hidemi Iwasaka, Yasuhiro Matsuda, Tsubasa
Morimoto, Mitsuhiro Uratani, Kosuke Okazaki, Yuichiro Fujimoto, and Satoshi
Nakamura. 2021. Multimodal Dataset of Social Skills Training in Natural Conversational Setting. In Companion Publication of the 2021 International Conference on Multimodal Interaction (ICMI ’21 Companion), October
18–22, 2021, Montréal, QC, Canada. ACM, New York, NY, USA, 5 pages.
https://doi.org/10.1145/3461615.3485425

1

INTRODUCTION

Social Skills Training (SST), which has been used in clinical populations for over 40 years, was developed based on conditioned
reflex therapy, psychotherapy by reciprocal inhibition, and social
learning theory [5, 20, 29]. SST improves the social skills that arenecessary for living socially at school or in company. SST can bedone with one trainer and one participant, or one trainer and several participants. In this paper, the target is one trainer and one
participant.
Figure 1 shows SST’s basic training flow. The trainer and theparticipant decide objective skills and the goal of SST. Then, thetrainer demonstrates a good example of the skill/goal by acting out
the situation him/herself. After that, the participant imitates thetrainer’s example. Then, positive or negative feedback is given by
the trainer. Based on the feedback, the participant repeats his/her
performance and tries to improve it if it is recommended. Homework is also assigned to participants to apply the trained skills fordaily communication; this process is called generalization.
Although SST is a well-known rehabilitation program that isfrequently used in medical fields, the amount of research on thecomputational analyses of SST interactions is limited [11, 25, 26].
However, we believe that computational analyses are required tofind objective and observable features for a more precise evaluationof participants’ social skills because such evaluations remain heavily
subjective. Actually, these computational approaches can be applied

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Saga, et al.

Figure 1: SST Flowchart
Figure 2: Recording device map
not only for participants’ evaluations but those for trainers, too.
Currently, there is no concrete evaluation method exists to develop
managing skills for trainers. Therefore, they must be evaluatedby other trainers. However, the evaluation axes greatly vary fromtrainer to trainer even if the evaluations are done by experienced
senior trainers. For better and more concrete evaluations, we havebeen working on this topic in a series of our researches [19, 25, 26].
In this paper, as a next step, we collected human-to-human SST
data with subjects of Autism Spectrum Disorder (ASD), Schizophrenia (SZ), and healthy control. Moreover, we clarified the significant
features in SZ and ASD, which have never been captured or analyzed in natural interactive SST settings. We recorded the interactions with multimodal capture devices for precise analyses. Notethat we only executed the yellow part in Figure 1 to control the
scenario for our research purposes.

2

RELATED WORKS

Hoque et al. collected job interview data to develop an automatedvirtual agent called MACH [11], which asks a variety of questions
with such interactive responses as nodding or mirroring smiles
followed by feedback. Throughout their research, they showed theeffectiveness of their interactive virtual agent and its feedback in
job interviews. Their team applied the method into the SST context
for elderly people and young people in their later works [1, 2].
Voleti et al. collected a dataset of three role-playing scenes[28]. They collected 87 clinical subjects (44 bipolar-i disorder, 43
Schizophrenia or schizoaffective disorders) and 22 healthy controlsthat participated in the SSPA tasks described by Patterson et al. [18].
They achieved 0.960 ROC scores just using text features. However,non-verbal skills should be considered for fair evaluations.
Compared to previous researches by Hoque and Voleti and ourteam, we targeted the identification of characteristic multimodal
features across SZ or ASD. Moreover, we collected the multimodal
data of the subjects and the trainers (conversational opponents),
including role-plays and feedback phases, to identify the significant
features of better trainers, which is currently unclear. Since thisstudy focuses on multimodal behaviors and interactions of humanhuman SST, we fixed most SST settings such as feedback strategies
across all sessions. Therefore, we won’t discuss method differences
between trainers or sessions.

3 DATASET
3.1 Setting
All of the data collection processes were approved by ethical committees in Nara Medical University and Nara Institute of Scienceand Technology. At the beginning of the recording, we explained
the procedure to the participants and got informed consent.

Figure 2 shows the recording device map where P stands forthe participant, T stands for the trainer. We used three video cameras and two Azure Kinects (K in Figure 2) to capture subtle social
signal of the participants and the trainers. Due to the Covid-19
pandemic, we set transparent partitions between the participants
and the trainers to prevent infections. We placed two cameras diagonally to avoid light reflection from the partitions to capture facial
expressions. Another camera was placed in the center to capture
the entire picture. We used Kinects to capture body movementswith a pretrained pose estimator based on depth images [24].
To investigate the interaction differences between different typesof mental or developmental difficulty, we recruited ASD and SZ
sufferers and healthy control subjects. Although equalizing theenvironments across all the recordings is preferred, our recordings
were done at three different places 1 to minimize participants’ mental workload. However, we paid much effort to equalize the otherenvironments as much as possible. Among many social skills, wetargeted four basic ones: listening, expressing positive or negative
feelings, refusing offers, and asking for favors. They are known as
the most fundamental skills defined by Bellack [6]. We also used
the same workflow and devices across every data collection.
We collected 49 pieces of the adult subjects’ data: 19 control(10 male and 9 female. The average age was 28.42. The standard
deviation of the age was 3.95), 15 ASD (10 male and 6 female. The
average age was 26.50. The standard deviation of the age was 5.67),
and 15 SZ (7 male and 8 female. The average age was 32.07. The
standard deviation of the age was 8.82). We also collected 33 piecesof children’s data for a better investigation of ASD. However, in thispaper, we mainly describe the adult subjects. All of the videos weresynchronized by a human annotator based on the handclap sign atthe beginning and the end of each role-play. All the utterances weretranscribed by a human annotator. In addition, we conducted another recording on eye-gaze differences with the same participantson the same day for future research, omitted here due to space
limitations. Note that since every subject was a Japanese native
speaker, the target language was Japanese.

3.2

Clinical assessments

We gathered several evaluation metrics to clarify the characteristicsof the subjects. For this dataset, we collected the Face Emotion Identification Test (FEIT) [14], the Kikuchi’s Scale of Social Skills: 18
items (KiSS-18) [15], Singelis scales for Independent-Interdependent
self-construal (Singelis) [22], the second edition of the Social Responsiveness Scale (SRS-2) [7], the second edition of the Autism
Diagnostic Observation Schedule (ADOS-2) [16], the Japanese version of the Brief Assessment of Cognition in Schizophrenia (BACS-J)
1 Nara Medical University Hospital, Heart-land Shigisan Clinic, and Nara Institute of

Science and Technology

Multimodal Dataset of SST in Natural Setting

ICMI ’21 Companion, October 18–22, 2021, Montréal, QC, Canada

Table 1: Evaluation metrics

Table 2: Audio features

Group

Control-adultASD-adult

Schizophrenia

FEIT
Kiss-18
Singelis
SRS-2
ADOS-2BACS-J
PANSS

✓
✓
✓
✓

✓
✓
✓
✓
✓
✓

✓
✓
✓

✓
✓

[13], and the Positive and Negative Syndrome Scale (PANSS) [12],depending on each symptomatic group. Table 1 shows the correspondence between the metrics and the groups.
FEIT, which assesses the emotional perception of facial emotions,
includes the facial images in greyscale of 19 different people with
one of six emotions: happiness, sadness, anger, surprise, fear, andshame. We included it since ASD struggle with emotion recognition
from facial images [9]. Kiss-18, which measures social skills levels,
is composed of 18 questions based on six social skill categories
defined by Goldstein [10]. This metric comprehensively measuressocial skills. Singelis is constructed of 30 questions on a 7-point rating scale. It was developed to measure how people view themselves
to others. SRS-2 is an evaluation metric for the severity of social
impairment that is composed of 65 questions. Although SRS-2 wasoriginally designed to assess potential ASD sufferers, it can also
differentiate among a variety of mental difficulties. Since its effectiveness has been investigated with the challenged and the healthy,
it is suitable for evaluating healthy people as well [7]. ADOS-2
is a semi-structured assessment that includes several play-based
activities for collecting information related to communication, social interactions, and restricted and repetitive behaviors associatedwith ASD. Since it doesn’t depend on any language levels, it canbe uniformly applied to kindergarten children and adults. BACSJ is a Japanese version of the Brief Assessment of Cognition in
Schizophrenia (BACS) that assesses the aspects of cognition foundto be most impaired and most strongly correlated with outcomesin SZ patients. Since not only SZ but also ASD include cognitive
impairments, we also applied BACS-J to ASD subjects. PANSS is an
instrument for typological and dimensional assessment for SZ thatis composed of standardized 30 items. It provides a balanced representation of positive and negative symptoms. With these metrics,
we plan to identify the characteristic differences among the clinicalgroups. Our annotator is currently compiling these metrics.
Since we tried a classification between SZ and control subjects in
section 4, we investigated the assessment score differences between
them. With the Mann-Whitney U rank test, we confirmed that there
weren’t significant differences (p>0.05) for Kiss-18, Singelis, andFEIT with p-values 0.94, 0.16, and 0.39 respectively.

3.3

Subjective evaluations

Furthermore, we subjectively evaluated the social skills of participants and trainers by other experienced trainers by watching thevideos afterward. We evaluated the participant acts with the following seven components: eye contact, body direction and distance,

Feature name

Description

F0, F1, F2, F3 Mean
F0, F1, F2, F3 SD
F0 Min, Max
F0 range
F1, F2, F3 BW
F2/F1, F3/F1 Mean
F2/F1, F3/F1 SD
Int range
Int SD

Mean frequency of F0, F1, F2, F3Standard deviation of F0, F1, F2, F3
Minimum and Maximum F0 frequency
Range (Max - Min) of F0
Average bandwidth of F1, F2, F3
Mean ratio of F2-F1 and F3-F1
Standard deviation of F2/F1 and F3/F1
Range (Max - Min) of intensities
Standard deviation of vocal intensity

facial expression, voice variation, clarity, fluency, social appropriateness for each task. In contrast, we evaluated the trainer actswith the following four components: appropriateness of positive
feedbacks, suggestions for improvement, appropriate non-verbal
communication as a good example, appropriate verbal communication as a good example. Every component was evaluated with a
5-point Likert scale. Each video was evaluated by two evaluators.
To validate the evaluation scores’ reliability, we calculated Cohen’s quadratic kappa scores. The reliability was confirmed withthe kappa score of 0.84 across entire subjective evaluations. There
were no significant differences in kappa scores between different
SST tasks. On the other hand, healthy controls were comparably
lower than SZ, and SZ was lower than ASD. We believe this indicated that the variability of social skills levels of healthy controls
was more diverse than other groups. Similarly, the kappa score forthe evaluations on participants was lower than the one on trainers.

4

CLASSIFICATION OF SCHIZOPHRENIAAND HEALTHY CONTROL

We validated the effectiveness of this dataset by training a classifier
using multimodal features. As an experimental study, we set theadult control subjects and the adult SZ subjects as the classification
targets. Unfortunately, we couldn’t include the ASD subjects in thisvalidation since we haven’t finished the annotation yet.

4.1

MethodWe used the random forest as the machine learning model. For its
input, we used audio-visual features calculated by existing softwareand preprocessed the data with the following procedure. First, audio data were separated from the video data. Then, we separated
the data into several segments according to SST phases, such as
role-plays or feedbacks. For the audio data, next, we eliminatedunvoiced and overlapped voiced segments between the trainer andthe participant. After that, we concatenated non-overlapped voiced
segments. Since the several files were too short for accurate calculation, we eliminated audio files shorter than five seconds. Note that
we didn’t use body joint position since we have not found a way tosynchronize the data at the boundary point of SST phases yet.
We used Praat for the audio feature calculation and OpenFace
for the visual feature calculation [3, 4, 27]. Tables 2 and 3 show the
input features, where F0 indicates the fundamental frequency ofvoiced segments, F1 to F3 indicate the first to the third formant of

ICMI ’21 Companion, October 18–22, 2021, Montréal, QC, Canada

Saga, et al.

Table 3: Visual features

Table 4: Classification results

Feature name

Description

FeatureAccuracy

Precision

Recall

F1-score

Pose_[Rx,Ry,Rz]_std
Smile_ratio

Standard deviation of head rotation
around (X:pitch,Y:yaw,Z:roll) axis
Ratio of Smiling frames

A
V
A+V

0.868
0.382
0.804

0.902
0.476
0.823

0.872
0.488
0.851

0.887
0.422
0.836

AU01
AU02
AU04
AU05
AU06
AU07
AU09
AU10
AU12
AU15
AU17
AU20
AU23
AU25
AU26
AU28
AU45

inner-brow raiser
outer-brow raiser
brow lowerer
upper-lid raiser
cheek raiser
lid tightenernose wrinkler
upper-lip raiser
lip-corner puller
lip-corner depressor
chin raiser
lip stretcher
lip tightenerlips parterjaw dropper
lip sucker
blinker

voiced segments, AUXX indicates facial Action Unit (AU) which
is a component of each facial expression. Since the backgroundnoise of the locations was different, perhaps the audio intensitychanged depending on the locations. Therefore, we didn’t include
the average, minimum, and maximum values of the intensity to
avoid the location differences, although the range and standard
deviation were included because they were potentially less affected.
Before being input into the model, all features were standardized
to have the mean of zero and the standard deviation of one. To
investigate which modality is dominant for this classification, we
set three different feature-set conditions: audio-only (A), visualonly (V), audio-visual (A+V). We trained the model ten times withrandom parameters and averaged the results to get a final result.

4.2

Results and discussion

Table 4 shows the classification results. The highest F1-score was
0.887. In terms of modality differences, the audio-only features
showed the best performance across all the metrics. In contrast, thevisual-only features showed significantly lower performance than
the audio-only. The audio-visual features showed an average performance. These classification results indicated the importance ofaudio features to differentiate two groups. Actually, prior research
reported vocal atypicalities in SZ [17]. Our classification resultswith different modality-sets supported them.
Furthermore, we investigated the feature importances for theclassification with random forest. Table 5 shows the top-3 featureimportances for the best model (audio-only). F3 BW shows thehighest importance value with over double the value of the others. It
is acknowledged that wide formant bandwidth induces a significant
reduction of the vowel identification rate [8, 21, 23]. In fact, weTable 5: Top-3 important features (audio-only)
Feature name

F3 BW

F2/F1 Mean

F1 BW

Importance

0.231

0.089

0.087

confirmed that the bandwidth of the SZ group was mostly wider
than the one of the control group by comparing each feature value.
Therefore, our result indicates the SZ group participants tend tohave less vowel intelligibility than the control group, which can bea part of communication problems.

5

CONCLUSION AND FUTURE WORK

We explained our SST data collection including subjects with mental
or developmental difficulty and classification with audio-visualfeatures. Our classification demonstrated the usefulness of our
natural conversational SST dataset. Although researchers have tried
to classify SZ in limited experimental situations, our classification
with this natural dataset achieved reasonably high performance.
Toward an automated SST system, several challenges remain.
First, an analysis of the trainers’ behavior is necessary to achieveeffective feedbacks. Such an idea is meaningful not only to develop
better automated SST trainers but objectively measure trainers’
skills, which is still difficult. Second, further investigations areneeded of the characteristic features of SZ. For years, trainers clearlyrecognize the existence of praecox feelings when they talk with SZ.
However, trainers couldn’t explain the feelings in detail because it
was ambiguous. We believe that our computational analyses will
provide answers to that question in the future.
As a limitation of our research, we couldn’t equalize relationships
between the trainers and the subjects. Since we couldn’t find anyother way to recruit participants with difficulties, we recruited themfrom the hospitals at which the trainers were working. Therefore,
the trainers and the disordered subjects had relationships before therecording, whereas the trainers and the control subjects didn’t havesince they were located through a subject recruiting service. Such
differences in locations and relationships are probably a limitationof our current research. In addition, we recruited four male andone female trainers for this research. Note that the results might beaffected by the imbalanced gender ratio of trainers.

ACKNOWLEDGMENTS
Funding was provided by the Core Research for the Evolutional
Science and Technology (Grant No. JPMJCR19A5) and the Japan
Society for the Promotion of Science (Grant Nos. JP17H06101 and
JP18K11437). We appreciate grateful help to other assistants in thedata collection, including laboratory co-workers and experienced
SST trainers.

Multimodal Dataset of SST in Natural Setting

REFERENCES[1] Mohammad Rafayet Ali, Seyedeh Zahra Razavi, Raina Langevin, Abdullah Al Mamun, Benjamin Kane, Reza Rawassizadeh, Lenhart K. Schubert, and Mohammad Ehsan Hoque. 2020. A Virtual Conversational Agent for Teens with Autism
Spectrum Disorder: Experimental Results and Design Lessons. In Proceedingsof the 20th ACM International Conference on Intelligent Virtual Agents (IVA ’20).
Association for Computing Machinery, New York, NY, USA, Article 2, 8 pages.
https://doi.org/10.1145/3383652.3423900[2] Mohammad Rafayet Ali, Kimberly Van Orden, Kimberly Parkhurst, Shuyang
Liu, Viet-Duy Nguyen, Paul Duberstein, and M. Ehsan Hoque. 2018. Aging andEngaging: A Social Conversational Skills Training Program for Older Adults. In
23rd International Conference on Intelligent User Interfaces (IUI ’18). Association
for Computing Machinery, New York, NY, USA, 55–66. https://doi.org/10.1145/
3172944.3172958[3] T. Baltrusaitis, A. Zadeh, Y. C. Lim, and L. Morency. 2018. OpenFace 2.0: Facial
Behavior Analysis Toolkit. In 2018 13th IEEE International Conference on Automatic
Face Gesture Recognition (FG 2018). 59–66.
[4] T. Baltrušaitis, M. Mahmoud, and P. Robinson. 2015. Cross-dataset learningand person-specific normalisation for automatic Action Unit detection. In 2015
11th IEEE International Conference and Workshops on Automatic Face and Gesture
Recognition (FG). 1–6.
[5] A. Bandura. 1969. Principles of behavior modification. Holt, Rinehart and Winston.
[6] Alan S. Bellack, Kim T. Mueser, Susan Gingerich, and Julie Agresta. 2004. Social
Skills Training for Schizophrenia: A Step-by-Step Guide (2 ed.). Guilford Press.
[7] John N. Constantino and Christian P. Gruber. 2012. Social Responsiveness Scale,
SRS-2 (2 ed.). Western Psychological Services.
[8] J. R. Dubno and M. F. Dorman. 1987. Effects of spectral flattening on vowel
identification. J Acoust Soc Am 82, 5 (Nov 1987), 1503–1511.
[9] O. Golan, S. Baron-Cohen, J. J. Hill, and Y. Golan. 2006. The "reading the mind
in films" task: complex emotion recognition in adults with and without autism
spectrum conditions. Soc Neurosci 1, 2 (2006), 111–123.
[10] I.L. Goldstein. 1986. Training in Organizations: Needs Assessment, Development
and Evaluation. Brooks/Cole publishing company.
[11] Mohammed Ehsan Hoque, Matthieu Courgeon, Jean-Claude Martin, Bilge Mutlu,
and Rosalind W. Picard. 2013. MACH: My Automated Conversation Coach. In
Proceedings of UbiComp ’13. Association for Computing Machinery, New York,NY, USA, 697–706. https://doi.org/10.1145/2493432.2493502[12] S. R. Kay, A. Fiszbein, and L. A. Opler. 1987. The positive and negative syndrome
scale (PANSS) for schizophrenia. Schizophr Bull 13, 2 (1987), 261–276.
[13] R. S. Keefe, T. E. Goldberg, P. D. Harvey, J. M. Gold, M. P. Poe, and L. Coughenour.
2004. The Brief Assessment of Cognition in Schizophrenia: reliability, sensitivity,and comparison with a standard neurocognitive battery. Schizophr Res 68, 2-3(Jun 2004), 283–297.
[14] S. L. Kerr and J. M. Neale. 1993. Emotion perception in schizophrenia: specificdeficit or further evidence of generalized poor performance? J Abnorm Psychol

ICMI ’21 Companion, October 18–22, 2021, Montréal, QC, Canada

102, 2 (May 1993), 312–318.
[15] Akio Kikuchi. 1988. The development of a social skills scale. 38 (1988), 67–68. In
Japanese.
[16] Catherine Lord and Michael Rutter. 2012. Autism Diagnostic Observation Schedule,
Second Edition. WPS.
[17] A. Parola, A. Simonsen, V. Bliksted, and R. Fusaroli. 2020. Voice patterns in
schizophrenia: A systematic review and Bayesian meta-analysis. Schizophr Res
216 (02 2020), 24–40.
[18] T. L. Patterson, S. Moscona, C. L. McKibbin, K. Davidson, and D. V. Jeste. 2001.
Social skills performance assessment among older patients with Schizophrenia.
Schizophrenia Research 48, 2–3 (3 2001), 351–360.
[19] Takeshi Saga, Hiroki Tanaka, Hidemi Iwasaka, and Satoshi Nakamura. 2020. Objective Prediction of Social Skills Level for Automated Social Skills Training Using
Audio and Text Information. In Companion Publication of the 2020 International
Conference on Multimodal Interaction. Association for Computing Machinery,
New York, NY, USA, 467–471. https://doi.org/10.1145/3395035.3425221[20] A. Salter. 1949. Conditioned reflex therapy. Creative Age Press.
[21] S Sekimoto. 1982. Effects of formant peak emphasis on vowel intelligibility infrequency compressed. Annual Bulletin of logopedics and phoniatrics 16 (1982).
[22] Theodore M. Singelis. 1994. The Measurement of Independent and Interdependent
Self-Construals. Personality and Social Psychology Bulletin 20, 5 (1994), 580–591.
https://doi.org/10.1177/0146167294205014[23] Quentin Summerfield, John Foster, Richard Tyler, and Peter J. Bailey. 1985. Influences of formant bandwidth and auditory frequency selectivity on identificationof place of articulation in stop consonants. Speech Communication 4, 1 (1985),
213–229. https://doi.org/10.1016/0167-6393(85)90048-2[24] T. Sych, C. Casey, and P. Meadows. [n.d.]. Azure Kinect DK Documentation.
https://docs.microsoft.com/en-us/azure/kinect-dk/ Last accessed: August 2021.
[25] Hiroki Tanaka, Hidemi Iwasaka, Hideki Negoro, and Satoshi Nakamura. 2020.
Analysis of conversational listening skills toward agent-based social skills
training. Journal on Multimodal User Interfaces 14, 1 (01 Mar 2020), 73–82.
https://doi.org/10.1007/s12193-019-00313-y[26] Hiroki Tanaka, Hideki Negoro, Hidemi Iwasaka, and Satoshi Nakamura. 2017.
Embodied conversational agents for multimodal automated social skills training
in people with autism spectrum disorders. PLOS ONE 12, 8 (08 2017), 1–15.
https://doi.org/10.1371/journal.pone.0182151[27] Vincent van Heuven. 2001. Praat, a system for doing phonetics by computer.
Glot International 5, 9/10 (2001), 341–345.
[28] Rohit Voleti, Stephanie Woolridge, Julie Liss, Melissa Milanovic, Christopher
Bowie, and Visar Berisha. 2019. Objective Assessment of Social Skills Using
Automated Language Analysis for Identification of Schizophrenia and Bipolar
Disorder. In Proceedings of Interspeech 2019. International Speech Communication
Association, 1433–1437. https://doi.org/10.21437/Interspeech.2019-2960[29] J. Wolpe. 1958. Psychotherapy by reciprocal inhibition. Stanford University Press.