Laugh at Your Own Pace: Basic Performance Evaluation of
Language Learning Assistance by Adjustment of Video Playback
Speeds Based on Laughter Detection
Naoto Nishida

University of Tsukuba
Ibaraki, Japannawta1998@gmail.com

Hinako Nozaki

University of Tsukuba
Ibaraki, Japan
nozaki@iplab.cs.tsukuba.ac.jp

Buntarou Shizuki

University of Tsukuba
Ibaraki, Japanshizuki@cs.tsukuba.ac.jp

1.0x

0.9x

If you canʼt explain it to…
Figure 1: System overview. If learners do not laugh at the punchlines of videos, our system judges that they do not comprehend
the video content, and gradually decreases the playback speed. Conversely, the speed increases if learners laugh.

ABSTRACT
Among various methods to learn a second language (L2), such as
listening and shadowing, Extensive Viewing involves learning L2 bywatching many videos. However, it is difficult for many L2 learners to smoothly and effortlessly comprehend video contents madefor native speakers at the original speed. Therefore, we developeda language learning assistance system that automatically adjusts
the playback speed according to the learner’s comprehension. Our
system judges that learners understand the contents if they laughat the punchlines of comedy dramas, and vice versa. Experimental
results show that this system supports learners with relatively low
L2 ability (under 700 in TOEIC Score in the experimental condition) to understand video contents. Our system can widen learners’
possible options of native speakers’ videos as Extensive Viewing
material.

CCS CONCEPTS• Human-centered computing → Human computer interaction (HCI); • Applied computing → Education.

KEYWORDS
Human-Computer Interaction, Computer-Assisted Language Learning, Extensive Viewing, Language, Learning, Facial Expression
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L@S ’22, June 1–3, 2022, New York City, NY, USA© 2022 Association for Computing Machinery.
ACM ISBN 978-1-4503-9158-0/22/06. . . $15.00
https://doi.org/10.1145/3491140.3528299

ACM Reference Format:
Naoto Nishida, Hinako Nozaki, and Buntarou Shizuki. 2022. Laugh at YourOwn Pace: Basic Performance Evaluation of Language Learning Assistanceby Adjustment of Video Playback Speeds Based on Laughter Detection. In
Proceedings of the Ninth ACM Conference on Learning @ Scale (L@S ’22),
June 1–3, 2022, New York City, NY, USA. ACM, New York, NY, USA, 6 pages.
https://doi.org/10.1145/3491140.3528299

1

INTRODUCTION

Among various methods to learn a second language (L2), Renandya
and Jacobs defined Extensive Viewing as “listening to large amountsof motivating and engaging materials which are linguistically appropriate over a while where they listen with a reasonable speed
for general understanding, with a focus on meaning rather thanform” [39]. Ivone and Renandya further defined it as “to be exposed
to a large amount of easily comprehensible and enjoyable materials presented in the target language over an extended period” asa kind of Extensive Listening [24]. Extensive Viewing has various
benefits as a learning method; learners can improve their listening fluency [7, 39], increase their listening vocabulary [15, 29, 30],and get used to liaisons, slang, and spoken forms of the target language [3, 40]. Besides, it is also superior to akin Extensive Learning
methods because engaging and real-life videos can maintain learners’ motivation towards learning [52], learners can start learning
whenever they want [8, 47], and captions can aid the comprehension for the contents [21, 28, 36]. Therefore, Extensive Viewing
recently gained the attention of language learners as a valuable andenjoyable method [24].
However, it is difficult for many L2 learners to view the learningmaterials at the rate at which native speakers do because of their
undeveloped reading and listening knowledge of the target language [5, 47]. As such, they usually face scenes where they cannot
fully understand the content at the original playback speed.

As easily comprehensible material is essential for Extensive Viewing [24], video materials for native speakers (e.g., drama series) at
the original speed are not suitable for L2 learners.
On the other hand, making L2 learners manipulate interfaces
to change the playback speed is also problematic. This is because
continuous manipulation over a long period can make learners lessmotivated [17], which is not suitable for Extensive Viewing as amethod that takes over months or years [32, 39, 47].
Therefore, we created a language learning assistance system thatenables L2 learners to effortlessly engage in long-lasting Extensive
Viewing of videos made by native speakers. This system can automatically adjust the playback speed of videos depending on thecomprehension of each L2 learner based on their facial expressions(Figure 1).
For this purpose, we first examined if learners act differently
depending on their comprehension. From our preliminary study, we
found that if learners laugh at the punchlines of videos, then they
comprehend the content, and vice versa. We then proved that our
proposed system is adequate for L2 learners with low proficiency.
Our research can broaden learners’ possible choices of materials
for Extensive Viewing.
Our contributions can be summarized as follows:• We found appropriate indicators that suggest learners’ comprehension at a specific time while viewing videos (i.e., gaze,facial expressions, and other gestural behaviors).
• We developed a novel method to support L2 learners by adjusting the playback speed according to the comprehensionof each learner.
• We evaluated the system via an experiment, revealing that
it is beneficial for learners whose L2 skills are relatively low(under 700 in TOEIC score in the experimental condition).

2 RELATED WORK
2.1 Existing Computer-Assisted Language
Learning Methods
In the context of Extensive Viewing, existing computer-assisted
language learning methods mainly focus on developing the vocabulary sizes of L2 learners. For instance, Hu et al. and Sakunkoo et
al. developed a system that enables learners to watch videos withinteractively translated captions, where learners hover a mouseon unknown L2 words [22, 43]. Fujii and Rekimoto developed asystem that detects English learners’ proficiency level to suggest amoderate number of translations [17].
However, there are few systems to support L2 learners using
playback speed control in Extensive Viewing. Besides, existing systems require a series of movements via mouse cursors from users tochange the speed of videos [11, 22, 48]. Extensive Viewing requires
continuous efforts towards learning from learners. Therefore, theworkload necessary for viewing should be reduced as much as
possible.
Thus, we developed a system that supports L2 learners throughvideo speed control and requires less workload.

2.2

Relationship between Playback Speed andLearner Comprehension

In terms of foreign language learning, Blau and Sugai et al. confirmed that a slower speech rate facilitates listening comprehension
among L2 learners [5, 49]. On the other hand, in terms of Massive Open Online Courses (MOOCs) in mother tongues, Kao et
al. confirmed that sped up lecture videos boost MOOC learners’
comprehension, while shortening the viewing time and raising theprobability of completing the course [25, 27].
From these results, we induced that if learners cannot understand the video contents well, slowing the video content would
be helpful because they can spend additional time processing theoral information, whereas if learners can understand them well,
speeding up the video content would benefit learners because they
can spare unnecessary time to watch and keep their concentration.

2.3

Reduction of Learners’ Workload

For learners maintaining a daily habit of Extensive Viewing, theworkload necessary for the manipulation should be reduced asmuch as possible. Fujii et al. and Song et al. used unconscious gazemovements or postures to predict learners’ L2 proficiency levels [17,45]. Similarly, Arakawa and Yakura used learners’ unconscious
reactions towards oral alterations to keep their attention during
video-based learning, while maintaining the mental workload at aconstant level [1].
Hence, we used learners’ unconscious behavior to alleviate theworkload necessary for learning. Specifically, we focused on whether
learners laugh when they face the punchline scenes of video material.

2.4

Apparatus for Extensive Viewing

One of the aims of Extensive Viewing is to increase the L2 learning time outside of the classroom [39, 47]. In addition, Extensive
Viewing requires long-lasting continuation on a daily basis [47].
Therefore, to reduce the workload required for the setup for learning anywhere and anytime, we should minimize the apparatus forExtensive Viewing.
Therefore, we examined features that we can capture via a builtin webcam of an ordinary laptop computer (MacBook Pro).

3IMPLEMENTATION

Through our preliminary study with 5 participants (all males, M=23.6,
SD=1.82, all of them are Japanese and learn English as a foreign
language), we discovered that people laugh at the punchlines ofcontent only when they understand the English video content well.
Figure 2 presents the total Action Unit 14 (AU14) values according
to the frame number of the experimental videos1 . Action Units aremeasures to capture human facial movements by the appearance on
their face [9, 12, 13]. AU14 corresponds to a movement of buccinator, which can be an index of smile and laugh [18, 50]. In the analysis
of our preliminary study, we confirmed a significant difference in
AU14 between when learners keep up with the contents and when
they cannot (p<.001 in Mann-Whitney U test, and effect size 0.22 in
1 https://drive.google.com/file/d/1dUrHqrXmw1APNtyVhdz-2tVeLOofoMbN/view?

usp=sharing

a

When people understand video contents
When people donʼt understand video contents

total
movie timenumber ofpunchlines

total
punchline time

8 min 42 sec

35

108.316 sec

8 min 44 sec

32

97.692 seccA

Values of AU14

punchline scenes
1

ON

B
2

a movie clipquiz

a movie clip

quiz

2

OFF

1
ON

C

9 clips and quizzes

b

2

1

OFF

2

1
OFF

quiz

D

2

ON

1
OFF

Frame Number

Figure 2: Values of Action Units 14 (AU14) of the 5 participants according to frame number. Red points represents
the case when learners understand the contents. Blue points
represents the case when they do not understand. Yellow
ranges are punchline scenes in the experimental video. In
the preliminary study, participants watched the video twice.
First, they watched it normally; second, while watching theyclassified when they can keep up with the story and when
they cannot.
Hedges’ g; we used Kolmogorov-Smirnov test for normality confirmation), whereas the other AUs, face tilt, and gaze direction did
not show a significant difference. Some participants also said that
if they cannot keep up with the story, their face gets strained, andthey cannot laugh at the punchlines of the contents. Additionally,
we prototyped a system that adjusts the playback speed of videos
depending on the viewer’s facial expressions and demonstrated it ata workshop in Japan [33]. We then ensured changing the playback
speed could help learners comprehend the contents.
Based on the result of that preliminary study, we implemented
a language learning assistance system that personalizes the playback speeds of video material based on laughter detection in thepunchlines of the videos.
We used Python 3.7.4, Perception for Autonomous Systems (PAZ)
library for facial expression detection, Selenium library for manipulation of the video player, and Laughter-Detection library for
detecting the punchlines of video materials [2, 10, 19]. We defined apunchline as a scene where a laugh track is inserted, assuming thatsituation comedy (i.e., content with laugh tracks) is the de facto
standard for Extensive Viewing [47, 52]. We chose video streaming sites (e.g., YouTube, Netflix) as a video player platform because
these sites employ a time stretch algorithm, which provides a steadypitch regardless of playback speed changes [37].
This system decreases the playback speed by 0.1x when the user
does not show a smile or laugh in punchline scenes in the videos,
and increases the speed by 0.1x when the user shows a smile orlaugh in punchline scenes. The playback speed ranges from 0.6x to
1.0x (original speed).

4

EXPERIMENT

We conducted an experiment to evaluate the system’s basic performance to help learners understand videos while keeping theworkload and enjoyment towards the content steady, which areessential factors for the continuation of Extensive Learning. We
selected English as the target language because of its prevalence,ON

30sec~60sec 20sec

Figure 3: Control configuration: a) control in two videos, b)
composition of a video, and c) control in groups.
Table 1: TOEIC scores in each group.

A
B
C
D

TOEIC score565 635 700
550 690 755
590 670 730
550† 670 680

755
760
790
785

855
815
845
880

Mean (SD)702.00 (111.2)
714.00 (101.8)
725.00 (99.87)
713.00 (111.9)† One person had not taken TOEIC. Thus, we hy-

pothesized their score from their statement.

making it easier to look for participants who learn English as aforeign language (EFL).

4.1

Participants

We recruited EFL learners whose English proficiency level is above
B1 in CEFR score [34], which is 550 in TOEIC score [41]. This isbecause Extensive Learning is a method mainly for people whoknow at least 3000 basic words [38, 42, 51], and people whose
English CEFR score is above B1 generally know more than 3000
words [4, 31, 46]. Twenty participants (4 females, 16 males, M=22.6,
SD=1.59, all Japanese) participated in this experiment. We asked
the participants to join this experiment with their ordinary eye
conditions and make their faces easy to see, such as brushing up
bangs, during the experiment. 8 participants joined the experimentwith naked eyes, 5 participants with glasses, and 7 participants withcontact lenses.

4.2

Apparatus and Location

We used a laptop PC (MacBook Pro 13 inch, Big Sur 11.6, 2.8 GHz
quad-core Intel Core i7, 16 GB memory). The built-in webcam was
a 720p FaceTime HD camera (1280 x 720 pixels). We avoided backlit
conditions to capture the face clearly. The distance between a participant’s face and the PC display was from 40 cm to 60 cm, whichis the recommended distance of PC manufacturer [35].

4.3

Configuration

To reduce bias from the video itself, we made two similarly constructed videos (V12 and V2 3 ). 578 words were spoken in V1, and469 words were spoken in V2. Each video was composed of 9 clips
2 https://drive.google.com/file/d/1LYx2rFDSLjlLNZo5Pa6qiUuhSweM_Y0W/view?

usp=sharing
3 https://drive.google.com/file/d/18cyV69l3x0ieA6xVUyg15MuHxcDOJKMz/view?
usp=sharing

a 8

Table 2: Time spent on each process.

2 lowest scores from each groupviewing timewith the system
634.5
44.62

4
2

We take group A (Figure 3 c) as an example to explain the procedure. First, participants answered a vocabulary test (VT1) whose
vocabulary appears in the following V1. The vocabulary test was
conducted to teach participants the difficult words necessary tokeep up with the story; therefore, we told participants the correct answer immediately after they finished answering. Second,
we let them read a series of quizzes (Q1) to be presented in thequiz time in V1 to ensure they had no vocabulary problems whileanswering. Third, participants viewed V1 with our proposal system,
answering Q1 at the same time. Fourth, participants answered VT1
again to be checked if they had no vocabulary issues while viewing
V1. Fifth, participants answered NASA-TLX, an assessment toolthat rates perceived workload [20]. After that, participants were
given the same series of workflows again under the condition of
V2, VT2, Q2, and without-system viewing instead of V1, VT1, Q1,
and with-system viewing. Participants were given 5 minutes rest
after answering NASA-TLX. At the end of the experiment, theyanswered a free answer sheet and SUS to assess usability [6]. We
told the participants to write about any unknown words in thevideos and quizzes. The whole experiment time was 64 minutes on
average. Whether a participant used the system was single-blinded.

4.5

Results

The time spent on each process is shown in Table 2. The time
participants viewed videos without the system was 523 seconds
on average, meaning they spent 1.2 times longer than they viewed
without the system.
Regarding the participants’ comprehension, as shown in Figure 4, learners with low L2 proficiency (the lowest 2 people fromeach group) could answer both V1 and V2 more correctly with oursystem (Q1:p<.005 and Q2:p<.05, the effect sizes are 2.69 and 1.71

0

5

88

88
0

5

85

85
5

5

84

0

81

5

79

0

with system(A,D)

78

5

76

5

75

0

75

0

73

0

70

0

TOEIC Score

70

0

69

0

67

5

67

0

63

5

59

0†

Procedure

55

55

4.4

b8

without system(B,C)

2 lowest scores from each group

Q2 Score

6
4
2

84
5

81
5

79
0

78
5

with system(B,C)

76
0

75
5

75
5

73
0

70
0

TOEIC Score

70
0

69
0

67
0

67
0

63
5

59
0

55
0

55
0

†

0
without system(A,D)

Figure 4: Relationship between TOEIC score and Q1 (a) or
Q2 (b). Relatively, participants with low TOEIC score hada higher score when they used our system. The green area
indicates the participants with the lowest and second lowest
scores from each group.
100

viewing with our system

viewing without our system

NASA-TLX scorefrom a sitcom Friends and 9 corresponding comprehension quizzes
about the clips (Figure 3 a and Figure 3 b). Each clip’s length wasapproximately from 60 to 90 seconds, and each quiz’s length was
20 seconds based on the example of listening quizzes of TOEIC
and TOEFL [16, 44]. Because we compare the difference in comprehension between when a participant uses our system and when
they do not, we divided the participants into 4 groups (Figure 3 c,Table 1). We allocated them according to their TOEIC score because
the reliability of the test is high (0.90) [23, 26], meaning that its
score almost certainly reflects learners’ L2 ability. In addition, toreduce the effects of the participants’ vocabulary ability, we aimed
to ensure every word in the video was known. To this end, we letthem read a series of quizzes shown in the videos and taught them
the unknown vocabulary spoken in the videos. We also had themread a complex vocabulary list. We took the 20 most rarely seenwords from each video with reference to the Corpus of Contemporary American English [14]. We ensured all participants knew thevocabulary in the videos and quizzes in our post-questionnaire.

0

0

56

reading quiz
beforehand
Q1
Q2
169.6 164.0
86.79 92.38

56
5

mean(s)SD(s)answering time on VT1, VT2
pre-viewing
post-viewing
VT1
VT2
VT1
VT2
111.8 138.6 44.86 48.37
31.27 56.57 13.02 19.51

Q1 Score

6

75

50

25

0

MentalDemandPhysical
DemandTemporal
DemandPerformance

Eﬀort

Frustration
Level

OverallFigure 5: NASA-TLX score and its 10% error range. This showsthat our system mostly does not worsen learners’ mental
workload. Besides, it significantly alleviates Temporal Demand and Effort.
respectively). This means that learners with low L2 proficiency
levels can understand videos more with our system.
In terms of mental workload, NASA-TLX shows lower scoresfrom perspectives other than Performance [20], showing significant
differences in Temporal Demand and Effort (Figure 5).
As for system usability, SUS scored 73.75 on average (SD=14.66),
which represents Good usability [6].

5DISCUSSION & FUTURE WORK

From our experiment, we confirmed that our system enhanced L2
learners’ comprehension of video content as a language learningmaterial. The effect was particularly significant for learners withlow proficiency (under 700 in TOEIC score in our experiment condition). We also proved that this system costs learners less mental
workload and provides Good usability.
In future work, we will increase the number of participants
with lower L2 proficiency (B1 or below in L2 proficiency). We
also plan to discover more appropriate ways to extract learners’
comprehensions. Besides, we will improve our system so that people
can speed up the playback rate past 1.0x to skip any scene they caneasily understand. This allows learners to spare time, leading toless workload for learning and the daily continuation of Extensive
Viewing.

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