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American Sign Language Recognition in Game
Development for Deaf Children
Helene Brashear 1,
Valerie Henderson 1
1Georgia Institute of
Technology
GVU Center
College of Computing
Atlanta, Georgia, USA
(brashear, sylee, vlh,
thad)@cc.gatech.edu
Kwang-Hyun Park 2,
Harley Hamilton 3,
2Korea Advanced Institute of
Science and Technology
Daejeon, Republic of Korea
akaii@robotian.net
Seungyon Lee 1,
Thad Starner 1
3Center for Accessible
Technology in Sign
Atlanta Area School for the
Deaf
Clarkston, Georgia, USA
hhamilto@doe.k12.ga.us
ABSTRACT
CopyCat is an American Sign Language (ASL) game, which
uses gesture recognition technology to help young deaf chil-
dren practice ASL skills. We describe a brief history of
the game, an overview of recent user studies, and the re-
sults of recent work on the problem of continuous, user–
independent sign language recognition in classroom settings.
Our database of signing samples was collected from user
studies of deaf children playing a Wizard of Oz version of the
game at the Atlanta Area School for the Deaf (AASD). Our
data set is characterized by disfluencies inherent in contin-
uous signing, varied user characteristics including clothing
and skin tones, and illumination changes in the classroom.
The dataset consisted of 541 phrase samples and 1,959 in-
dividual sign samples of five children signing game phrases
from a 22 word vocabulary.
Our recognition approach uses color histogram adapta-
tion for robust hand segmentation and tracking. The chil-
dren wear small colored gloves with wireless accelerometers
mounted on the back of their wrists. The hand shape in-
formation is combined with accelerometer data and used
to train hidden Markov models for recognition. We evalu-
ated our approach by using leave–one–out validation; this
technique iterates through each child, training on data from
four children and testing on the remaining child’s data. We
achieved average word accuracies per child ranging from
91.75% to 73.73% for the user–independent models.
Categories and Subject Descriptors
K.4.2 [Social Issues]: Assistive technologies for persons
with disabilities; I.2.7 [Natural Language Processing]:
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ASSETS’06, October 22–25, 2006, Portland, Oregon, USA.
Copyright 2006 ACM 1-59593-290-9/06/0010 ...$5.00.
Language models; I.5.4 [Pattern Recognition]: Imple-
mentation: interactive systems
General Terms
Human Factors, Languages
Keywords
Sign Language, ASL, Recognition, Game
1. INTRODUCTION
Ninety percent of deaf children are born to hearing par-
ents who may not know sign language or have low levels
of proficiency with sign language [5]. Unlike hearing chil-
dren of English–speaking parents or deaf children of sign-
ing parents, these children often lack the access to language
at home which is necessary for developing linguistic skills.
Often these children’s only exposure to language is from
signing at school. Linguists have identified a “critical pe-
riod” for language development - a period during which a
child must be exposed to and immersed in a language. It is
important that children are exposed to sufficient language
examples during this period to aid in the development of
life long language skills. Although originally thought to ex-
ist only for spoken languages, research has shown that this
critical period also applies to ASL acquisition [14, 16].
Hearing children have a multitude of educational software
products to enhance the language instruction they receive
at school. This software is designed for use both at home
and at school. A 1999 Kaiser Family Foundation report esti-
mates that 17% of children ages 2-7 and 37% of children ages
8-13 play computer games on any given day [19]. Interactive
ASL software is usually concentrates on students’ ability to
receive and comprehend language rather than on their abil-
ity to generate language independently. Two examples of
this software are Con-SIGN-tration [10] and Aesop’s Fables:
Four Fables [18]. During Con-SIGN-tration, children play a
memory game which involves matching cards bearing ASL
signs to cards with English words. Aesop’s Fables presents
several of Aesop’s Fables interpreted into sign and then the
child is asked a series of comprehension questions in English
79
following the stories. However, to our knowledge, no games
currently on the market allow children to communicate with
the computer via their native language of ASL. Games that
do prompt children to mimic signs have no measure of eval-
uation to help the child improve the clarity and correctness
of their signs. This lack of repetition with feedback prevents
children from benefiting fully from the software.
1.1 Sign Language Recognition
Sign language recognition is a growing research area in
the field of gesture recognition. Research on sign language
recognition has been done around the world, using many
sign languages, including American Sign Language [26, 22,
2], Korean Sign Language [11], Taiwanese Sign Language
[13], Chinese Sign Language [4, 6], Japanese Sign Language
[20], and German Sign Language [1]. Many sign language
recognition systems use Hidden Markov Models (HMMs) for
their abilities to train useful models from limited and poten-
tially noisy sensor data [6, 22, 26].
Sensor choices vary from data gloves [13] and other tracker
systems to computer vision techniques using a single cam-
era[22], multiple cameras, and motion capture systems [25]
to hand crafted sensor networks[8].
Starner et. al. demonstrated a continuous sign recogni-
tion system that performed at 98% accuracy with a 40 ASL
sign vocabulary, in a lab environment using HMMs, using a
simple grammar [22]. Further work was done to explore dif-
ferent sensor configurations and increase both the flexibility
and mobility of the system [2, 8, 15]. These studies showed
that the accuracy of ASL recognition can be increased by
combining computer vision techniques with a small number
of accelerometers.
2. THE SYSTEM
CopyCat is an educational computer game that utilizes
computer gesture recognition technology to develop Ameri-
can Sign Language (ASL) skills in children ages 6-11. Copy-
Cat’s goal is to encourage signing in complete phrases and
to augment a child’s educational environment with a fun
and engaging way to practice language skills. CopyCat con-
sists of the hardware necessary for gesture recognition and
the game software. The game is interactive – with tutorial
videos demonstrating the correct signs, live video (provid-
ing input to the gesture recognition system and feedback to
the child via the interface), and an animated character ex-
ecuting the child’s instructions. The game focuses on the
practice and correct repetition of ASL phrases and allows
the child to communicate with the computer via ASL.
We are using an iterative design approach for the develop-
ment of CopyCat [12, 7]. Iterative design is a cyclic process
of design work, prototyping, testing and evaluation. This
approach has allowed us to continually improve the game
design and include the users throughout the entire design
process. Throughout the testing process children are asked
questions about their game experience. Each cycle results
in a testing session at AASD, as well as a post-testing eval-
uation of the game and our collected data. The evaluation
provided us with a checklist of strengths and weaknesses to
bring to the next iteration.
Since no suitable previous ASL recognition engine exists
for this project, a Wizard of Oz (WOz) approach is used.
The Wizard of Oz (WOz) technique is an evaluation method
which uses a human “wizard” to simulate the functional-
Figure 2: System setup. a) the workspace b)
IEEE1394 camera c) colored gloves with wireless
accelerometers in green-colored pockets d) wireless
mouse
ity that will eventually be provided by the computer. The
Wizard is situated out of sight of the subject, receives the
subject’s input, and controls the system manually, emulat-
ing the missing functionality of the system [3]. During the
WOz simulation, a participant performs tasks using the pro-
totype. The subject is not aware of the Wizard’s presence
and believes that he is using a fully functioning system. In
our scenario, while the child plays the game, a human Wiz-
ard simulates the computer recognizer and evaluates the cor-
rectness of the player’s sign with the help of the interpreter.
By using a Wizard of Oz technique we allow for itera-
tive, parallel development tracks. Design work can continue
in each of the research areas and be evaluated periodically
through user testing. The parallel track spans several areas:
•Game interface design is a combination of Human
Computer Interaction (HCI) design principles and it-
erative design practices. Research issues include game
and character design, presenting ASL information in
a game context, collecting data on ASL signing, re-
sponding appropriately to signs, and linguistic consis-
tency in the game’s spatial layout.
•Language development is both an educational and
linguistic focus. Research issues include designing prin-
cipled signed interactions in the game and evaluating
student game performance in a meaningful and useful
way, as well as longer term evaluation of the develop-
mental impact of the system on the children’s language
skills.
•ASL recognition development focuses on machine
learning. Research issues include modeling the chil-
dren’s signs, recognized the children’s signs as correct
or incorrect, identifying and modeling disfluencies in
sign, and identifying and acting on non-scripted com-
munication with the characters in the game.
2.1 System Design
The system’s configuration is shown in Figure 2. The
workspace area (shown in Figure 2A) consists of a com-
puter monitor, a desk for the mouse, and a chair. Users
80
Figure 1: Screen shot of ASL game. a) tutor video b) live camera feed c) attention button d) animated
character and environment e) action buttons
Figure 3: Wireless accelerometer board
can navigate parts of the game (such as requesting a tuto-
rial video) using the mouse. Data from the children’s sign-
ing is recorded using an IEEE 1394 video camera (shown
in Figure 2B) and using wireless accelerometers (shown in
Figure 3) mounted in colored gloves (shown in Figure 2C).
The colored gloves help aid the computer vision algorithms
used on the video data and hold the wireless accelerometers
in a stable position on the wrist. The wrist–mounted ac-
celerometers provide additional information which can aid
the recognition task [2].
The sensor configuration was chosen to satisfy specific re-
quirements. The game should be able to run in a variety of
locations and lighting environments. The system should not
be expensive and should be easy to configure in a school en-
vironment. Computer vision has been successfully used for
sign recognition, and cameras are available, inexpensive and
durable. Data gloves are instrumented gloves that measure
flexion and movement. They are appealing to sign language
recognition projects because of the quantity and detail of
information, but they tend to be very expensive and are not
commonly available in child’s sizes. Additionally, they are
generally not designed for the stresses of classroom use.
3. ASL RECOGNITION SYSTEM
3.1 Data Set
With the assistance of educational technology specialists
and linguists, we developed a list of appropriate phrases
which are assigned to actions in the game. Phrases were
selected with the goal of having three and four signs, us-
ing age-appropriate vocabulary (English translations listed
in Table 1). This structure was chosen as part of the goal of
encouraging the linguistic transition from single sign utter-
ances to complete phrases. A vocabulary was chosen that
was consistent with what they used in their classes and com-
patible with system constraints. The recognition engine is
currently limited to a subset of ASL which includes single
and double handed signs, but does not include more com-
plex linguistic constructions such as classifier manipulation,
facial gestures, and level of emphasis. Each phrase is a de-
scription of an encounter for the game character, Iris the
cat. The students can warn of predators, such as “go chase
snake” or identify the location of a hidden kitten, such as
“white kitten behind wagon”.
We collected the data in nine days with five children ages
9-11 at the Atlanta Area School for the Deaf in Clarkston,
Georgia. Collecting data for use in statistical pattern recog-
nition is both time consuming and tedious because a large
number of samples must be collected and then labeled. We
use a Wizard of Oz configuration and a “push-to-sign” mech-
anism to collect and segment relevant data samples [12, 7].
During game play, the main character Iris is asleep. The
child must click to wake Iris, sign the phrase, and click to
end the interaction. We use “push-to-sign”, which segments
the samples by the start and stop clicks of the mouse during
81
Action
Button Level 1 Level 2 Level 3
#1 Go chase snake Go chase snake Go chase snake
#2 Go chase snake Go chase spider Go chase spider
#3 Go chase snake Go chase spider Go chase alligator
#4 White kitten behind wagon White kitten under chair Whitekitteninflowers
#5 Black kitten under chair Blackkitteninflowers Black kitten in bedroom
#6 Orange kitten in flowers Orange kitten in bedroom Orange kitten on wall
#7 Blue kitten in bedroom Bluekittenonwall Blue kitten behind wagon
#8 Green kitten on wall Green kitten behind wagon Green kitten under chair
Table 1: Phrases used for ASL game (English translation)
game play. This push-to-sign mechanism is similar to those
found in many speech recognition systems (called push-to-
talk for speech) and allows our ASL recognition system to
perform recognition on only pertinent phrases of the chil-
dren’s sign. The data can be automatically labeled at a
phrase level using information from the game. We use this
method to remove both out-of-context and unscripted sign-
ing, as well as ignore the child’s out–of–game comments.
Thus the segmentation and labeling is done concurrently
with the data collection. Post–processing and labeling of
the data is still required, but the workload and boundary
accuracy is greatly improved.
The Wizard of Oz setup and push–to–sign mechanism in
the game allowed us to collect a large amount of signing
during testing. This data set is unusual because it consists
of samples of children signing and interacting naturally with
the game. Most sign language data sets are collected in the
lab under controlled conditions with well–enunciated sign-
ing. Our data set of signing contains a variety of signing
inflections and emphases as well as sign accents common
among the children.
3.2 Image Processing
Our data consists of video of the user’s signing and ac-
celerometer data from glove-mounted, wireless accelerome-
ters. In our system, we require the children to wear small
pink-colored gloves. This bright color is easily identified
by a computer vision algorithm. Tracking skin tones can
be particularly problematic for computer vision in uncon-
strained environments. Additionally, it is difficult to distin-
guish when the hands perform signs near the face. Many
algorithms have been suggested to segment hand region ro-
bustly, even under illumination change [17, 28, 23, 24, 21].
However, some of them address only a narrow range of il-
lumination change, and some results do not guarantee real-
time processing (at least 10 fps with 720 ×480 sized images
in our system) or robustness for long image sequences of ges-
tures. Some methods extract similar color regions as well as
hand color region, and the performance strongly depends on
the result in the first image frame.
In our approach, the image pixel data is converted to HSV
color space and used to create histograms for segmentation
of the hand region and background, as shown in figure 4.
HSV histograms are used to produce a binary mask using a
Bayes classifier [21] and noise is removed by morphological
filters including size filtering and hole filtering. The posi-
tion of the desk and the colored gloves provide a significant
marker for starting the gesture recognition; the light color
of the desk provides a high contrast environment. The chil-
dren click the mouse to start and end each phrase, which
provides both location with color cues, as well as a start
and end gesture. From these cues, we can extract the mouse
hand region well for the first frame of the image sequence
by simply applying a threshold.
We initially use the hand segmentation to create the start-
ing histogram. Each frame is a segmentation cycle, which
provides feedback to the system and helps enhance the dis-
crimination of the color models. HSV histograms are up-
dated with a weight value ω(0 <w<1), based on the
obtained mask and then the histograms are normalized:
H←(1 −ω)H+ωHnew
where Hdenotes the histogram value for each bin [21].
Figure 4 shows the hand tracking process for later frames.
The segmentation of the hand region and the update of HSV
histograms are the same as the procedure in the first frame.
To find both hands in the binary mask, we consider the size
of hand shapes and the distance between the center position
of the candidate blobs, as well as the hand positions in the
previous image frame.
Figure 5 shows the results of the image processing for sev-
eral image sequences processing occurs at 48.574ms/f rame
(20.59 fps) in a laptop computer with 1GHz processor. We
found that the tracking results were acceptable, even when
the child wears a shirt with similar color patterns.
3.3 Accelerometer Processing
Our accelerometers (shown in 3) are a custom in–house de-
sign created for wearable, wireless sensing. These small wire-
less sensor platforms provide a Bluetooth serial port profile
link to three axes of accelerometer data. The accelerome-
ter is sampled by a PIC microcontroller at approximately
100Hz. The sensors run on a standard camera battery.
Each accelerometer data packet consists of four values – a
16 bit hexadecimal sequence number and three 10 bit hex-
adecimal values (one each for the X, Y, and Z axes). The
axis values represent the gravitational affect of acceleration.
Once the data packets are read from the accelerometer they
are post-processed. First they are synchronized with our
video feed so that the accelerometer data packets for each
hand are associated with the correct video frames. Second,
the data is smoothed to account for variable number of pack-
ets associated with each frame. Because of sampling issues,
each video frame can vary in accelerometer packets by one
or two packets.
82
Figure 5: Segmented hand regions in the image sequences. green: right hand, red: left hand, cyan: occluded
hands
3.4 Feature Vectors
Our feature vectors consist of the combination of both vi-
sion data and accelerometer data. The accelerometer data
consists of (x, y, z ) values for accelerometers on both hands.
The vision data consists of the following hand shape charac-
teristics: the change in x, y center positions between frames,
mass, the length of the ma jor and minor axes, eccentricity,
orientation angle of the major axis, and direction of the ma-
jor axis in x, y offset. The camera captures images at 10
frames a second and each frame is synchronized with the
averaged accelerometer values. For recognition we adopt
left to right HMMs with four states.
3.5 Experiment
The data collected represents each of the five children
playing all three levels of the game at least five times each.
Each sample consists of one signed phrase from the game.
Samples were initially classified as a correctly or incorrectly
signed phrase, based on feedback from our consultants from
AASD. The “correct” samples were those that were signed
correctly according to game play. These samples were fur-
ther pruned to removed any samples which were evaluated as
correct for content but had problems with the signing such
as false starts, fidgeting or poorly formed signing. This set
of good samples were then labeled to create a transcript of
the signed phrase. The final data set represented 541 signed
sentences and 1,959 individual signs.
We used the Georgia Tech Gesture Toolkit (GT 2K) [27]
to train and test our system. GT 2Kadapts HTK (Hidden
Markov Model Toolkit) for gesture recognition. HTK pro-
vides HMMs in the context of a language infrastructure for
use in speech recognition [9]. We used the language tools to
train a single model for each sign and then ran additional
training for context (equivalent to speech triphone model-
ing). This procedure allowed us to create stable individual
models, and then to combine those models to represent the
co–articulation effects of continuous signing. HTK also pro-
vides an infrastructure for rule–based and statistical gram-
mars.
ASL is a structured language complete with a grammar,
vocabulary, and other linguistic features. Thus, the appli-
cation of a relevant grammar together with statistical word
models can provide a practical solution to remove ambiguity
due to disfluency of the deaf children in their signing. Table
4 shows the grammar adopted to our system given in HTK
expression, where sil0, sil1, sil2 and sil3 denote silence mod-
els by which transitional motions and pauses between words
can be segmented in the signing. Coarticulation is the effect
that words or signs have on each other when they proceed
or succeed one another. The ordering of the silences helps
maintain consistency for modeling coarticulation effects.
3.6 Results
3.6.1 User Dependant Models
We evaluated our approach using several different meth-
ods. Table 2 shows results from testing user–dependent
models – models which are trained and tested using a sin-
gle child’s data. The user–dependent models were generated
by training on 90% of the samples randomly selected from
a single child’s dataset and testing on the other 10%. This
was done 100 times for each child and averaged. These show
how well the models perform for training and testing for in-
dividual users. We achieved an average word accuracy of
93.39% for the user–dependent models.
3.6.2 User Independent Models
Table 3 shows results from testing for user–independent
models using leave–one–out validation. User–independent
models can be used to recognize signs from multiple children.
The user–independent models were generated by training
on a dataset consisting of four children and testing on the
83
Data Set Word Accuracy Sentence Correctness
Mean StdDev Mean StdDev
Participant 1 (90/10 split) 94.1105% 3.162212 69.3663% 11.95803
Participant 2 (90/10 split) 91.4754% 4.283267 69.3663% 11.95803
Participant 3 (90/10 split) 94.9271% 2.404333 70.9118% 11.48396
Participant 4 (90/10 split) 95.6477% 2.61991 74.6389% 12.1403
Participant 5 (90/10 split) 90.8016% 3.868346 55.779% 12.2334
Table 2: Comparison of recognition results for user–dependent models
Data Set Word Accuracy Sentence Correctness
Leave one out: Participant 1 87.33% 51.69%
Leave one out: Participant 2 76.90% 38.24%
Leave one out: Participant 3 92.62% 61.61%
Leave one out: Participant 4 90.94% 56.76%
Leave one out: Participant 5 83.60% 44.90%
Leave one out: Average 86.28% - stdev 6.29 50.64% - stdev 9.3
Table 3: Comparison of recognition results of user–independent models
Figure 4: Hand segmentation for the first image
frame
other child’s dataset. This is done for each child. This helps
us understand how the models generalize across users. We
achieved an average word accuracy of 86.28% for the user–
independent models.
3.6.3 All samples
We achieved on average 92.96% of accuracy in word-level
with 1.62% of standard deviation when we chose samples
across all samples and users (we trained and tested using
data from all students). All 541 sentence examples were
randomly divided into a set of 90% training sentences and
a set of 10% independent test sentences. The test sentences
were not used for any portion of the training. We repeated
this training and test cycle 100 times using HTK and calcu-
lated the average of the recognition accuracy.
3.6.4 Summary
We ran three sets of experiments with our data sets: user–
dependant, user–independent, and across all samples. The
user–dependent models show how well recognizer performs
for a single individual. All of the students signing can be
modeled fairly well, with a greater than 90% word accuracy.
However, when recognizing sentences, words can be inserted
and deleted which creates errors that lower sentence accu-
racy. The range in word accuracies from 90.8% to 95.6%
shows a strong variation in the user’s signing samples – some
are clearly modeled better than others.
Participant four has the highest word accuracy and the
second lowest standard deviation on the tests. These re-
sults can be an indicator that participant four probably had
clear, consistent signing. Participant two the second lowest
word accuracy and the highest standard deviation. When
used as the test set for leave–one–out validation, partici-
pant two scored the worst against the models. Participants
three and four were the top two performers for word accura-
cies and standard deviation in both tests. Participants that
signed clear and consistent data (evident by high word accu-
racies and low standard deviations over the user dependent
models) were also well modeled by the more general user
independent models.
The limited size of the participant pool restricts the gener-
84
alizations that can be drawn from the experiment, but these
experiments show interesting trends that should be investi-
gated for larger population sizes. Increasing the number
of users for both creating and testing models will give a
broader idea of the generalization. There is a tension be-
tween the usefulness of generalization and added accuracy
of user–specific training; this can be seen in speech recog-
nition packages that ship with models trained from large
populations and allow the user to help train the models by
providing additional training samples.
The leave–one–out validation shows how the models gen-
eralize to users they have never seen. The wider range of
word accuracies from 76.9 % to 92.6% show the impact that
including or excluding certain students from the training
set can affect the outcome. The consistency in performance
by participants in the user–dependent and user–independent
tests indicates that the user–independent models are doing
a good job of generalizing. The independent models are
generalizing well, even across models of varying qualities.
4. CONCLUSIONS
We present continued work on a computer game designed
to help children practice their ASL skills and encourage
their linguistic development. Linguistically, the system is
designed to help children practice their vocabulary and en-
courage them to generate phrases which convey complete
thoughts and ideas. CopyCat provides a new domain for
the sign language recognition community and has provided a
unique data set of native signers interacting naturally with a
computer system. The recognition engine represents an im-
portant step towards the recognition of conversational sign
in contrast to other systems which largely use scripted sign
collected in controlled laboratory environments.
We have expanded the functionality of our previous sys-
tems [22, 2, 15] to handle non-scripted, live signing which
includes pauses and variable coarticulation effects. We show
progress with the gesture recognition component of the game.
We use a hand segmentation algorithm which is robust against
illumination change and guarantees a real-time processing.
We also adopted a strong grammar to alleviate the effects
of disfluencies and to achieve a 92.96% word accuracy. Our
dataset is unique in the sign recognition community because
it uses signers conversing in a spontaneous, unscripted man-
ner. These results show the feasibility of the system and
provide a platform for further developing the recognition
capabilities.
4.1 Future Work
Though this work has extended the functionality of our
recognition system, it is clear that there is much work to
do. The dataset collected for our experiments is both excit-
ing and challenging. This evaluation uses samples selected
from the dataset for their correctness and clarity in signing.
These samples are used for building models and recognizing
signs. Our next challenge is enabling the recognition engine
to deal with the disfluencies present in otherwise linguis-
tically correct samples. Disfluencies of importance include
long pauses, fidgets, hesitations and false starts in signing.
We have completed the next round of iterative develop-
ment with a user study at AASD. The study included an-
other round of interface development and data collection,
as well as pre–study and post–study linguistic evaluations
to begin to explore the learning effects of the system. Pre-
liminary analysis of the data from this study has been ex-
tremely informative. We are currently focused on classifying
disfluencies and out–of–band communication by the children
during game play to provide more informed models and in-
crease recognition accuracy. The full analysis of this study
will provide us with further insight to educational value of
the system, as well as increase our data bank for further
recognition engine work.
5. ACKNOWLEDGEMENTS
This work is supported by the NSF, Grants # 0093291, #
0511900, and the RERC on Mobile Wireless Technologies for
Persons with Disabilities, which is funded by the NIDRR of
the US Dept. of Education (DoE), Grant # H133E010804.
The opinions and conclusions of this publication are those of
the grantee and do not necessarily reflect those of the NSF
or US DoE.
Special thanks to students and staff at the Atlanta Area
School for the Deaf for their generous help with this project.
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$predator = snake |spider |alligator ;
$color1 = white |green |blue ;
$color2 = black |white |green ;
$color3 = orange |black |white ;
$color4 = blue |orange |black ;
$color5 = green |blue |orange ;
$sentence = $color1 sil1 kitten sil2 behind wagon
|$color2 sil1 kitten sil2 under chair
|$color3 sil1 kitten sil2 in flowers
|$color4 sil1 kitten sil2 in bedroom
|$color5 sil1 kitten sil2 on wall ;
( sil0 go sil1 chase sil2 $predator sil3 |sil0 $sentence sil3 )
Table 4: Strong grammar adopted to the system
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