# Waveform Phasicity Prediction from Arterial Sounds through Spectrogram
Analysis using Convolutional Neural Networks for Limb Perfusion Assessment

Adrit Rao  Greene Middle School
Palo Alto, CA, USA
<EMAIL_ADDRESS>Kevin Battenfield, RVT  Stanford University
Stanford, CA, USA
<EMAIL_ADDRESS>
Oliver Aalami, MD  Stanford University
Stanford, CA, USA
<EMAIL_ADDRESS>
###### Abstract

Peripheral Arterial Disease (PAD) is a common form of arterial occlusive
disease that is challenging to evaluate at the point-of-care. Hand-held
dopplers are the most ubiquitous device used to evaluate circulation and
allows providers to audibly ”listen” to the blood flow. Providers use the
audible feedback to subjectively assess whether the sound characteristics are
consistent with Monophasic, Biphasic, or Triphasic waveforms. Subjective
assessment of doppler sounds raises suspicion of PAD and leads to further
testing, often delaying definitive treatment. Misdiagnoses are also possible
with subjective interpretation of doppler waveforms. This paper presents a
Deep Learning system that has the ability to predict waveform phasicity
through analysis of hand-held doppler sounds. We collected 268 four-second
recordings on an iPhone taken during a formal vascular lab study in patients
with cardiovascular disease. Our end-to-end system works by converting input
sound into a spectrogram which visually represents frequency changes in
temporal patterns. This conversion enables visual differentiation between the
phasicity classes. With these changes present, a custom trained Convolutional
Neural Network (CNN) is used for prediction through learned feature
extraction. The performance of the model was evaluated via calculation of the
F1 score and accuracy metrics. The system received an F1 score of 90.57% and
an accuracy of 96.23%. Our Deep Learning system is not computationally
expensive and has the ability for integration within several applications.
When used in a clinic, this system has the capability of preventing
misdiagnosis and gives practitioners a second opinion that can be useful in
the evaluation of PAD.

###### Index Terms:

Waveform Phasicity, Handheld Doppler, Deep Learning, Spectrogram,
Convolutional Neural Networks

##  I Introduction

Peripheral Arterial Disease (PAD) [1] is the narrowing or blockage of arteries
supplying the lower extremities due to atherosclerosis (Figure 1). According
to the Center for Disease Control (CDC) over 6.5 million people who are 40
years or older have PAD in the United States alone [2]. If not diagnosed and
treated early, PAD can lead to limb loss. After clinical examination for a
pedal pulse, the most common point-of-care tool utilized for evaluation of PAD
is a low-cost hand-held doppler [3]. When pointed at the direction of blood
flow of a target artery, this device allows the provider to audibly ”listen”
to the circulation. Providers are then expected to subjectively assess whether
the sound characteristics are consistent with Monophasic, Biphasic, or
Triphasic waveforms to diagnose the severity of PAD. This is a technically
challenging procedure as waveforms can be difficult to predict. Thus when
manually assessing these sounds mistakes can be made leading to misdiagnoses.
With any question in sound interpretation, practitioners will often refer
patients for further testing to a formal vascular ultrasound laboratory which
can lead to delays in definitive treatment. Within the vascular laboratory,
the Ankle-Brachial Index (ABI) is calculated and/or direct arterial ultrasound
imaging can be performed to diagnose the severity of PAD. For this, large and
expensive devices are utilized.

Figure 1: CDC PAD Artery Illustration [2]

Taking into account the subjectivity when assessing arterial sounds and
predicting waveform phasicity, there is a clear need for an effective method
for practitioners to cross-check their prediction and increase their
confidence in order to provide more timely treatment of PAD. This paper
presents a novel Deep Learning system that has the ability to predict waveform
phasicity through analysis of hand-held doppler sounds. Deep Learning is
becoming increasingly popular in the medical field for its ability to mimic
the human brain structure, learn from data and make clinically useful
predictions [4].

With current computational resources, Deep Learning is being widely utilized
for multiple tasks. Most clinical implementations of Deep Learning solutions
are centered around image classification and object detection for medical
imaging purposes [4]. Our method employs similar Deep Learning techniques but
for the classification of arterial sounds. This classification is made
possible by firstly converting input sound into a spectrogram image. This is
done to visualize features and convert frequency and amplitude variance into
visual temporal changes. With this conversion, visual differentiation is
present between the phasicity classes opening up the ability for
classification through a Deep Learning approach. A custom Convolutional Neural
Network (CNN) [5] trained on spectrogram images spanning the three waveform
phasicity classes is used for prediction. The CNN model revealed high accuracy
rates when tested on new data. This system can enhance the capabilities of
low-cost hand-held dopplers and aid practitioners at the point-of-care leading
to more timely clinical decisions. Figure 2 shows the three phasicity
waveforms. The Triphasic waveform consists of three phases, Biphasic has two
phases and the Monophasic waveform has one phase. The spectrogram visual
differentiation is similar to the waveforms visual differentiation.

Figure 2: Waveform Phasicity Comparison

##  II Related Work

A large amount of research has been conducted around the usage of Deep
Learning, Machine Learning (ML) and Computer Vision (CV) based techniques and
algorithms for the interpretation of data to classify cardiovascular
conditions and diseases. We have highlighted a paper that utilizes a sound
classification system for the classification of heart sounds (eg., murmurs).
Li et al. proposed the use of a custom trained CNN for the classification of
heart sounds as either abnormal or normal [6]. This research utilized the
PhysioNet cardiology dataset [7]. They used various signal processing
techniques for the plotting of Mel-frequency Cepstral Coefficients (MFCC).
They utilized a custom CNN to analyze the MFCC’s and make the binary
classification. The proposed algorithm received around a 72.1% to 86.8%
accuracy rate in classification.

Our method differentiates from Li et al. in two main factors. Firstly, our
method targets peripheral arteries affected by PAD and not the heart itself.
Thus our research required different means for both data collection,
generation, and model development. Additionally, in our opinion, making a
binary classification such as normal or abnormal likely does not provide a
detailed enough result for practitioners to interpret and utilize. Our system
predicts waveform phasicity, a widely utilized metric for the diagnosis of PAD
and other cardiovascular diseases. With a predicted phasicity, practitioners
can differentiate between mild, moderate, and severe arterial disease. This
can help in making a more robust diagnosis. Our system has also achieved an
accuracy rate of over 90%.

##  III Methods

The following order and methodology has been used to develop and validate our
full end-to-end sound classification system. Firstly, audio data was collected
and labeled within a clinical setting by a vascular technologist during formal
ABI examinations. After the collection of data, spectrogram images were
generated by inputting all collected sound files through a signal processing
conversion method. This converted frequency changes into temporal changes. As
the dataset was not very large in size, artificial data augmentations were
added to increase dataset size and possible learned features. After data
preparation, a Deep Learning model was developed and constructed through a
Transfer Learning [8] approach enabling high accuracy rates and less training
time on lower amounts of data. Lastly, the model was validated with a separate
set of doppler audio data through calculation of the F1 score and accuracy
metrics. Figure 3 depicts the fully constructed sound classification system
flow. The end-to-end system works by converting input sound into a spectrogram
image and then sending the image through a custom trained CNN which predicts
the waveform phasicity through learned visual features.

Figure 3: Full Sound Classification System Flow

As follows are each of the development stages consisting of a detailed
explanation which walks through the exact methodology as well as well as
specific tools and formulas used for implementation of the full sound
classification system.

###  III-A Data Collection

Hand-held doppler sounds were generated using the Parks Flo-Lab 2100 doppler
machine pencil doppler [9]. Four-second recordings were taken during formal
vascular lab studies in patients being evaluated for PAD. An iOS application
was developed to efficiently and securely create and label the recordings
through an iPhone within the clinic. The vascular technologist performing the
study labeled the waveforms using the Parks Flo-Lab digital waveform display
as a robust reference to maintain accuracy and data consistency. The iOS
iPhone application was developed within Apple’s Xcode Integrated Development
Environment (IDE) with the Swift 5 programming language. The app was linked to
a Cloud database via Google’s Firebase [10] allowing for real-time and secure
data transmission. Within the Firebase system, a real-time Database [10] was
created for storage of input values such as waveform phasicity and arterial
position, and a Storage bucket was created for storage of sound files.
Phasicity and artery names were also appended to the sound file titles for
fast referencing of data for cross-checking.

After data was collected within the database, the Python 3 programming
language and the Pyrebase third-party data extraction library was used to
create a data downloading script utilized for downloading all files from the
real-time database and storage bucket at once. Shown in Figure 3, is the
dataset distribution after all data was extracted from the database. A total
of 268 sound files were collected. The dataset was split into an 80% training
and a 20% testing standard split. This was to ensure data was available after
model training for validation/testing of the system.

Figure 4: Dataset Distribution Chart

Diverse data with background interference and noise was intentionally included
in the final dataset. This was to ensure that in later stages, the model would
potentially develop resilience to these factors as they will likely be present
in a clinical setting due to noise from other machines and tools.

###  III-B Data Generation

All sound files were converted into spectrogram images that represent
frequency changes visually over the four-second time span. The color variance
in the spectrogram represents amplitude changes. These changes clearly
visually differentiate the Monophasic, Biphasic, and Triphasic classes. Shown
in Figure 4, are spectrograms representing each of the three phasicity
classes. The Monophasic class does not have a lot of definition in the
amplitude variance but in the Biphasic and Triphasic classes the amplitude
variance increases showing clear visual class differentiation. This
differentiation is a representation of the increased blood flow and
pulsatility present in the Biphasic and Triphasic waveform phasicity classes.
Also, note that interference, as well as background noise, is present in the
selected spectrogram samples but differentiation remains.

Figure 5: Spectrogram Phasicity Comparison

Sound file to spectrogram conversion was implemented in the Python 3
programming language with the Librosa library [11]. Librosa is a popular
library for sound and signal processing as it holds a lot of useful features
within a minimal and simplified codebase. Spectrogram generation was done
within a loop function. This function converted input sound into a spectrogram
image by sending it through a Short-time Fast Fourier Transform (STFT) [12].
The STFT is a widely used and standard conversion method for spectrogram
generation. At a high level, this type of Fast Fourier Transform (FFT) [13]
divides a time signal into shorter equal segments/windows, runs a standard
FFT, and plots the spectrogram in the time-frequency domain. Shown in Equation
1 is the mathematical formula for the STFT. In this formula, the signal is
represented as $x[n]$ and the window as $w[n]$. The standard FFT converts the
input signal from its original domain into a frequency domain representation.
Doing this within the STFT method allows for spectral plotting. After the
generation of spectrograms, all images were converted into a [224, 224]
constant size as this is required by the specific model developed in later
stages.

$\mathbf{STFT}\\{x[n]\\}(m,\omega)\equiv
X(m,\omega)=\sum_{n=-\infty}^{\infty}x[n]w[n-m]e^{-j\omega m}$ (1)

###  III-C Data Augmentation

After all spectrogram images were generated, the dataset was artificially
augmented to increase the size and possible knowledge gained by the model in
later stages. The Keras Python library was used for adding data augmentations
as sequential layers. Keras is a widely used framework for Neural Network
construction as well as image pre-processing. One simple data augmentation was
added due to the system taking in clean spectral images in the end-stage.
Randomized zoom (20%) was chosen as the primary augmentation for this dataset.
Randomized zoom maintains valuable features as well as orientation. Figure 5
shows an example Triphasic spectrogram image with randomized zoom applied.
Amplitude variance is still visible in the image. This can possibly be
valuable.

Figure 6: Randomized Zoom Data Augmentation

###  III-D Model Development

Deep Learning is emerging as a powerful technology that is being used widely
in the medical field to aid practitioners. Deep Learning gives computers the
ability to learn from data and make clinically useful predictions by mimicking
the human brain’s neural structure and enabling the development of Artificial
Neural Networks (ANNs). CNNs, a form of Deep Neural Networks (DNNs) have
revealed high accuracy rates when analyzing visual imagery due to their unique
capability of extracting features through convolutions. As with many medical
projects, our dataset was small, and training a CNN from scratch would not be
ideal and high accuracy rates would not be achievable. To compensate for the
lack of data, a Transfer Learning approach was leveraged. Transfer Learning is
a popular Deep Learning methodology that allows for the retraining/relearning
of existing models. These models usually have deep architectures consisting of
multiple layers and have been trained on large amounts of images. The prior
knowledge gained from the model helps with the differentiation of new classes
thus gaining the ability to achieve high accuracy rates on minimal amounts of
data. This approach also enables faster training time (fewer
epochs/iterations) compared to developing a Neural Network from scratch with
no prior knowledge.

Inception V3 [14] is a popular CNN model architecture that has received fairly
high accuracy for image classification problems (ImageNet). Inception V3
builds upon previous Inception CNN versions due to its ability to reduce
computation and enable deep networks through dimensionality reduction
techniques. Inception V3 is a vast network architecture consisting of multiple
layers of convolution, pooling, concat, dropout, etc. The Inception V3 model
utilized for this project has gained prior knowledge through being trained on
around a million images from the ImageNet dataset/competition [15]. Because of
this, the model has gained weights that can help with the differentiation of
newly trained classes. To accommodate the new spectrogram image dataset, two
layers were added to the end of the Inception V3 architecture. The modified
Inception architecture consists of both a Flatten layer and a Dense layer for
final probability class prediction. The Flatten layer converts the output of
the original Inception V3 architecture into a one-dimensional representation.
The Dense layer is used for final probability class prediction through the
softmax activation function (Equation 2). Softmax is commonly used in Neural
Networks for multiple class predictions. Our softmax function contained three
nodes for the phasicity classes.

$\mathbf{\sigma(\vec{z})_{i}}=\frac{e^{z_{i}}}{\sum_{j=1}^{K}e^{z_{j}}}$ (2)

The CNN model was constructed and implemented sequentially in Keras.
Compilation and training were done with Tensorflow [16]. The Google Colab Pro
Integrated Development Environment (IDE) [17] was leveraged for Cloud
development allowing for fast model training with large amounts of available
compute. The Python 3 programming language was utilized. Within Colab, the
Tesla P100 GPU [18] was leveraged. The modified Inception V3 architecture is
shown in Figure 7. The full Inception V3 model was downloaded from Google
Storage with the ImageNet weights initialized.

Figure 7: Modified Inception V3 Model Architecture

###  III-E Model Training

The model was compiled first and then trained across 10 epochs using
Tensorflow. When training a Neural Network from scratch, epochs can be up to
100 but in our case due to the usage of Transfer Learning, 10 epochs was
sufficient for gaining high accuracy. The model was fed both training and
testing data. For compilation, the Adam optimizer [19] was utilized. Sparse
Categorical-cross entropy was the loss function chosen. At each epoch in model
training F1 score and accuracy, metrics were calculated as shown in Equation 4
and Equation 5. Additionally, the model’s training and testing accuracies were
graphed over the 10 epochs along with loss. This data when interpreted can
show us if the model is learning from the data properly and if overfitting is
present.

$\mathbf{F1}=\frac{2*Precision*Recall}{Precision+Recall}=\frac{TP}{TP+\frac{1}{2}(FP+FN)}$
(3)

$\mathbf{Accuracy}=\frac{TP+TN}{TP+TN+FP+FN}$ (4)

##  IV Results and Discussion

After the system was fully developed, validation was done through calculation
of the F1 score and accuracy metrics. The F1 score is a standard validation
metric that takes into account both precision and recall. It is widely used in
the validation of Deep Learning and ML systems. Accuracy was also calculated
and used in the validation of the system. Accuracy is the percentage of
predictions that the model made correctly through all of the testing data. Our
system received a validation accuracy of 96.23% and an F1 score of 90.57%. As
these metrics are over 90%, it clearly shows that the model is highly accurate
in predicting phasicity through sound and that the proposed sound
classification system is accurate.

To further validate the system and its learning rate, graphs created during
model training were interpreted. These graphs represent training and
validation accuracy and loss over the 10 training epochs. As the loss is
consistently decreasing through the iterations consistently, overfitting is
not present and the model has a good learning rate. The accuracy graph
consistently increases without any dips indicating that the model is learning
at a good rate. Additionally, the validation accuracy as well as training
accuracy are close together. Figure 9 shows the loss and accuracy graphs over
the 10 epochs.

Similar to most medical studies, data is limited and we could have benefited
from having more data from multiple sites to help avoid any potential biases
intrinsic to the patient population characteristics. FFT analysis to classify
doppler signals has been attempted but never for doppler sounds of the tibial
vessels [20]. The power of such analysis is particularly evident in the
setting of a calcified tibial vessel which does not allow for accurate ankle-
brachial index measurement.

Figure 8: Model Accuracy and Loss Graphs

##  V Conclusion

We have successfully developed an end-to-end sound classification system that
can predict arterial doppler waveform phasicity with high accuracy. Vascular
specialty care is limited as are vascular labs to formally rule out PAD. This
model and method can be implemented in primary care, podiatry, and wound care
clinics to provide a more objective peripheral vascular assessment and assist
in real-time treatment decisions and avoid delays in care. Our next steps are
to study the prediction of ABIs and to integrate this model into a low-cost
mobile application and hardware platform which can be used at the point-of-
care ubiquitously.

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euro@! | risk |  0 /8 | 0/10 | 1/9
euro@! | gorge |  0 /8 | 0/10 | 1/9
euro@! | montero |  0 /8 | 0/10 | 1/9
euro@! | steel |  0 /8 | 0/10 | 1/9
sharp@! | schart | 1/1 |  0 /1 | 0/1
crisps@! | cris | 1/7 |  0 /7 | 0/6
euro@! | europ | 1/8 |  0 /10 | 0/9
euro@! | e | 1/8 |  0 /10 | 0/9
20 euro@! | pantro | 1/2 |  0 /0 | 0/0
20 euro@! | gantero | 1/2 |  0 /0 | 0/0
i@! baby look@! | babyokae | 1/1 |  0 /0 | 0/0
sharp@! | shard |  0 /1 | 1/1 | 0/1
sharp@! | shared |  0 /1 | 0/1 | 1/1
peanut@! | painut | 1/1 |  0 /1 | 0/1
live@! | leaves |  0 /49 | 0/50 | 1/50
live@! | lift |  0 /49 | 0/50 | 1/50
live@! | lit |  0 /49 | 0/50 | 1/50
carry@! me | karimi | 1/1 |  0 /0 | 0/0
snowmans@! | snowman’s |  0 /1 | 0/1 | 1/1
ran@! | rawing | 1/2 |  0 /2 | 0/2
ran@! | run | 1/2 |  0 /2 | 0/2
i’d@! | id | 1/1 |  0 /1 | 0/1
loves@! klettern@g | loveklittan | 1/1 |  0 /0 | 0/0
f like@! | flag |  0 /1 | 0/0 | 1/1
f like@! | ffly | 1/1 |  0 /0 | 0/1
has a@! | hasa | 1/1 |  0 /0 | 0/0
wool@! | will |  0 /1 | 0/1 | 1/1
found@! | foun | 1/3 |  0 /3 | 0/3
always@! | _ |  0 /13 | 0/13 | 1/13
always@! | alwayse | 1/13 |  0 /13 | 0/13
liked@! it | like tit | 1/2 |  0 /0 | 0/1
loves@! | lovs | 1/10 |  0 /13 | 0/14
piano cause@! | pianobecause | 1/1 |  0 /0 | 0/0
loves@! | u | 1/10 |  0 /13 | 0/14
mir@! | p | 1/1 |  0 /1 | 0/1
a pet@! | sepet | 1/2 |  0 /0 | 0/0
invisible@! | injivible | 1/6 |  0 /5 | 0/6
invisible@! | invisiable | 1/6 |  0 /5 | 0/6
invisible@! | invisib | 1/6 |  0 /5 | 0/6
more@! | move |  0 /25 | 0/24 | 1/24
pirates@! | pirattes | 1/12 |  0 /12 | 0/11
pirates@! | piraten | 1/12 |  0 /12 | 0/11
pirates@! | pairats | 1/12 |  0 /12 | 0/11
pirates@! | parat | 1/12 |  0 /12 | 0/11
universal@! | inuniversal | 1/1 |  0 /1 | 0/1
having@! | it |  0 /2 | 1/2 | 0/2
having@! | have ing | 1/2 |  0 /2 | 0/2
snowmans@! | snow mans | 1/1 |  0 /1 | 0/1
more@! | work |  0 /25 | 0/24 | 1/24
invisible@! | in ziba |  0 /6 | 0/5 | 1/6
more@! | moore |  0 /25 | 0/24 | 1/24
more@! | smalre | 1/25 |  0 /24 | 0/24
more@! | or | 1/25 |  0 /24 | 0/24
more@! | moreise | 1/25 |  0 /24 | 0/24
more@! | mor | 1/25 |  0 /24 | 0/24
uhm something’s@! | usomething | 1/1 |  0 /0 | 0/0
invisible@! | in wiesb |  0 /6 | 1/5 | 0/6
invisible@! | in the weevil |  0 /6 | 1/5 | 0/6
a pet@! | supet | 1/2 |  0 /0 | 0/0
roles@! | rooms |  0 /5 | 1/5 | 0/5
roles@! | less |  0 /5 | 1/5 | 0/5
what@! | whare | 1/50 |  0 /49 | 0/51
rathe@! | right |  0 /1 | 0/1 | 1/1
rathe@! | ra | 1/1 |  0 /1 | 0/1
china@! | kina |  0 /4 | 1/4 | 0/4
china@! | _ |  0 /4 | 0/4 | 1/4
china@! | chinese |  0 /4 | 0/4 | 1/4
china@! | hina |  0 /4 | 0/4 | 1/4
china@! | chine | 1/4 |  0 /4 | 0/4
china@! | chiney | 1/4 |  0 /4 | 0/4
on the wetboard@! | under wut bard | 1/1 |  0 /0 | 0/0
rather@! | rite | 1/16 |  0 /16 | 0/16
rhine@! | rin | 1/1 |  0 /1 | 0/1
what@! | wol d | 1/50 |  0 /49 | 0/51
what@! | whatch | 1/50 |  0 /49 | 0/51
difference@! | differentes | 1/7 |  0 /7 | 0/7
vaise@! | waste |  0 /2 | 0/2 | 1/2
what@! | will | 1/50 |  0 /49 | 0/51
points@! | _ |  0 /5 | 1/5 | 0/5
points@! | pints | 1/5 |  0 /5 | 0/5
rathe@! | rate |  0 /1 | 1/1 | 0/1
where@! old | whey ad | 1/1 |  0 /0 | 0/0
roles@! | rolls |  0 /5 | 1/5 | 0/5
why@! is | whyis | 1/1 |  0 /0 | 0/0
roles@! | rose |  0 /5 | 0/5 | 1/5
roles@! | rollo so |  0 /5 | 0/5 | 1/5
loves@! | love see |  0 /10 | 0/13 | 1/14
roles@! | roms | 1/5 |  0 /5 | 0/5
roles@! | ros | 1/5 |  0 /5 | 0/5
roles@! | wlles | 1/5 |  0 /5 | 0/5
roles@! | rols | 1/5 |  0 /5 | 0/5
roles@! | rools | 1/5 |  0 /5 | 0/5
rather@! | red |  0 /16 | 1/16 | 0/16
am you@! | hamyou | 1/1 |  0 /0 | 0/0
rather@! | wait |  0 /16 | 1/16 | 0/16
loves@! | logs |  0 /10 | 0/13 | 1/14
rather@! | neither |  0 /16 | 0/16 | 1/16
rather@! | rean | 1/16 |  0 /16 | 0/16
rather@! | rat | 1/16 |  0 /16 | 0/16
rather@! | riathen | 1/16 |  0 /16 | 0/16
rather@! | rathethe | 1/16 |  0 /16 | 0/16
loves@! | _ |  0 /10 | 0/13 | 1/14
towards@! | twords | 1/1 |  0 /1 | 0/1
pirates@! | pots |  0 /12 | 0/12 | 1/11
play@! | 321 |  0 /75 | 1/73 | 0/73
ladder@! | later |  0 /2 | 0/2 | 1/2
swim@! | swam |  0 /17 | 1/19 | 0/18
thinks@! | _ |  0 /2 | 1/2 | 0/2
thinks@! | things |  0 /2 | 1/2 | 0/2
thinks@! | tings | 1/2 |  0 /2 | 0/2
white@! | ride |  0 /3 | 1/3 | 0/3
white@! | write |  0 /3 | 0/3 | 1/3
white@! | wite | 1/3 |  0 /3 | 0/3
ladder@! | leter | 1/2 |  0 /2 | 0/2
wide@! | whid | 1/4 |  0 /4 | 0/4
head@! | hed | 1/6 |  0 /6 | 0/5
angel@! | anjen | 1/1 |  0 /1 | 0/1
teleport@! | _ |  0 /2 | 1/3 | 0/3
teleport@! | and |  0 /2 | 0/3 | 1/3
teleport@! | deleport | 1/2 |  0 /3 | 0/3
you too@! | youto | 1/1 |  0 /0 | 0/0
can@! | carrying |  0 /48 | 1/51 | 0/52
can@! | cannot |  0 /48 | 0/51 | 1/52
can@! | black and |  0 /48 | 0/51 | 1/52
head@! | the | 1/6 |  0 /6 | 0/5
head@! | had | 1/6 |  0 /6 | 0/5
pitch@! | page |  0 /2 | 1/2 | 0/2
night@! | nigt | 1/1 |  0 /1 | 0/1
live@! | leaf |  0 /49 | 0/50 | 1/50
stick@! | k | 1/1 |  0 /1 | 0/1
live@! | li | 1/49 |  0 /50 | 0/50
swim@! | an | 1/17 |  0 /19 | 0/18
live@! | liv | 1/49 |  0 /50 | 0/50
swim@! | in |  0 /17 | 0/19 | 1/18
swim@! | whim |  0 /17 | 0/19 | 1/18
eh eine@! bremse@g | a inebemseafa | 1/1 |  0 /0 | 0/0
swim@! | swimmer |  0 /17 | 0/19 | 1/18
head@! | aha | 1/6 |  0 /6 | 0/5
swim@! | scream |  0 /17 | 0/19 | 1/18
seeing@! | sing | 1/3 |  0 /3 | 0/3
seeing@! | seeng | 1/3 |  0 /3 | 0/3
swim@! | swing |  0 /17 | 1/19 | 0/18
i’li@! | i | 1/1 |  0 /1 | 0/1
writing@! | riting | 1/2 |  0 /2 | 0/2
head@! | hair |  0 /6 | 1/6 | 0/5
head@! | het | 1/6 |  0 /6 | 0/5
can@! | then |  0 /48 | 0/51 | 1/52
stayed in@! | say tid | 1/1 |  0 /0 | 0/0
pirates@! | parents |  0 /12 | 0/12 | 1/11
play@! | plague |  0 /75 | 0/73 | 1/73
healsy@! | houssi | 1/1 |  0 /1 | 0/1
prejure@! | prefer |  0 /1 | 1/1 | 0/1
prejure@! | peter |  0 /1 | 0/1 | 1/1
prejure@! | preather | 1/1 |  0 /1 | 0/1
press@! | pres | 1/1 |  0 /1 | 0/1
lotterlebenbook@! | lotta leben book |  0 /1 | 1/1 | 0/1
lotterlebenbook@! | lotalabook |  0 /1 | 0/1 | 1/1
lotterlebenbook@! | loter lam book | 1/1 |  0 /1 | 0/1
amazings@! | amasing | 1/1 |  0 /1 | 0/1
healsy@! | healthy |  0 /1 | 1/1 | 0/1
play@! | globe |  0 /75 | 0/73 | 1/73
play@! | plan |  0 /75 | 0/73 | 1/73
carry@! | cary | 1/2 |  0 /3 | 0/3
carry@! | kari | 1/2 |  0 /3 | 0/3
pirates@! | pirat |  0 /12 | 1/12 | 0/11
pirates@! | piratas |  0 /12 | 1/12 | 0/11
pirates@! | irt |  0 /12 | 0/12 | 1/11
play@! | c |  0 /75 | 0/73 | 1/73
healsy@! | housy |  0 /1 | 0/1 | 1/1
wel@! | ri | 1/1 |  0 /1 | 0/1
kicked@! | kick | 1/3 |  0 /3 | 0/3
dollar@! | thus a |  0 /16 | 0/16 | 1/17
can@! | caing | 1/48 |  0 /51 | 0/52
can@! | ca un | 1/48 |  0 /51 | 0/52
can@! | cana | 1/48 |  0 /51 | 0/52
can@! | cant | 1/48 |  0 /51 | 0/52
dollar@! | dolla |  0 /16 | 1/16 | 0/17
play@! | playd | 1/75 |  0 /73 | 0/73
dollar@! | standard |  0 /16 | 0/16 | 1/17
dollar@! | doubles |  0 /16 | 0/16 | 1/17
dollar@! | tall and |  0 /16 | 0/16 | 1/17
play@! | place |  0 /75 | 0/73 | 1/73
dollar@! | doas | 1/16 |  0 /16 | 0/17
dollar@! | dol | 1/16 |  0 /16 | 0/17
dollar@! | doll | 1/16 |  0 /16 | 0/17
play@! | pla | 1/75 |  0 /73 | 0/73
play@! | polly | 1/75 |  0 /73 | 0/73
play@! | plae | 1/75 |  0 /73 | 0/73
kick@! | kickt | 1/7 |  0 /7 | 0/7
play@! | pay |  0 /75 | 0/73 | 1/73
they@! | that |  0 /38 | 1/38 | 0/38
they@! | he |  0 /38 | 1/38 | 0/38
floor@! | flur | 1/1 |  0 /1 | 0/1
vanille@! | one |  0 /7 | 0/8 | 1/6
he@! | okay |  0 /137 | 1/134 | 0/132
free@! | we |  0 /27 | 1/26 | 0/28
free@! | freei |  0 /27 | 0/26 | 1/28
free@! | her |  0 /27 | 0/26 | 1/28
he@! | him |  0 /137 | 1/134 | 0/132
free@! | twthr | 1/27 |  0 /26 | 0/28
free@! | fr | 1/27 |  0 /26 | 0/28
free@! | fre | 1/27 |  0 /26 | 0/28
spittle@! | spide | 1/1 |  0 /1 | 0/1
spittle@! | _ |  0 /1 | 1/1 | 0/1
straight@! | strat | 1/1 |  0 /1 | 0/1
pupil@! | poppit | 1/3 |  0 /3 | 0/3
dues@! | dos | 1/1 |  0 /1 | 0/1
tier@! | cheer |  0 /1 | 1/1 | 0/1
tier@! | t |  0 /1 | 0/1 | 1/1
tier@! | ter | 1/1 |  0 /1 | 0/1
roger@! | rather |  0 /1 | 1/1 | 0/1
roger@! | ruche |  0 /1 | 0/1 | 1/1
roger@! | rater | 1/1 |  0 /1 | 0/1
the rice@! | deris | 1/1 |  0 /0 | 0/0
a tree@! 12 tree | 3123 | 1/1 |  0 /0 | 0/0
this@! friends | disferent | 1/1 |  0 /0 | 0/0
he@! | to |  0 /137 | 0/134 | 1/132
be@! | beef |  0 /22 | 0/21 | 1/22
be@! | by |  0 /22 | 0/21 | 1/22
be@! | bit |  0 /22 | 0/21 | 1/22
he@! | i |  0 /137 | 0/134 | 1/132
he@! | hi |  0 /137 | 0/134 | 1/132
be@! | beu | 1/22 |  0 /21 | 0/22
be@! | live | 1/22 |  0 /21 | 0/22
be@! | bei | 1/22 |  0 /21 | 0/22
curtain@! | curen | 1/1 |  0 /1 | 0/1
teff@! | have | 1/1 |  0 /1 | 0/1
flower@! | _ |  0 /8 | 1/8 | 0/8
flower@! | flowing |  0 /8 | 0/8 | 1/8
flower@! | wflow | 1/8 |  0 /8 | 0/8
notice@! | knows |  0 /1 | 1/1 | 0/1
notice@! | know this |  0 /1 | 0/1 | 1/1
notice@! | now | 1/1 |  0 /1 | 0/1
teff@! | it has |  0 /1 | 1/1 | 0/1
teff@! | tap |  0 /1 | 0/1 | 1/1
somethings@! amazing | some things ameing | 1/1 |  0 /0 | 0/0
rabbit@! | it’s |  0 /3 | 1/3 | 0/3
rabbit@! | aeuit | 1/3 |  0 /3 | 0/3
mi@! | my |  0 /2 | 1/2 | 0/2
pupil@! | pope |  0 /3 | 0/3 | 1/3
this@! have@! | the staff |  0 /1 | 0/0 | 1/1
this@! have@! | the self | 1/1 |  0 /0 | 0/1
begin@! | speaking |  0 /2 | 1/2 | 0/2
begin@! | bigging | 1/2 |  0 /2 | 0/2
chance@! | chancs | 1/1 |  0 /1 | 0/1
pupil@! | poopy |  0 /3 | 1/3 | 0/3
grandpa@! | grandpac | 1/1 |  0 /1 | 0/1
mi@! | be |  0 /2 | 0/2 | 1/2
napkin@! | nepkin | 1/1 |  0 /1 | 0/1
mi@! | the |  0 /2 | 0/2 | 1/2
pupil@! | pew pew |  0 /3 | 1/3 | 0/3
mi@! | b | 1/2 |  0 /2 | 0/2
for@! to li | fatally |  0 /1 | 0/0 | 1/1
for@! to li | faterly | 1/1 |  0 /0 | 0/1
data@! | _ |  0 /1 | 1/1 | 0/0
data@! | vanada | 1/1 |  0 /1 | 0/0
vanille@! | _ |  0 /7 | 1/8 | 0/6
geometry@! | jeomtr | 1/1 |  0 /1 | 0/1
art@! | ard | 1/4 |  0 /4 | 0/4
rabbit@! | rabet | 1/3 |  0 /3 | 0/3
if you could@! | i woulth | 1/1 |  0 /0 | 0/0
cart@! | card | 1/1 |  0 /1 | 0/1
gummy bears@! | gummybears | 1/1 |  0 /0 | 0/0
wine@! | _ |  0 /1 | 1/1 | 0/1
wine@! | win | 1/1 |  0 /1 | 0/1
home why@! | homeyeah | 1/1 |  0 /0 | 0/0
i winter why@! | winterwo | 1/1 |  0 /0 | 0/0
winner@! | viner |  0 /1 | 0/1 | 1/1
winner@! | winger | 1/1 |  0 /1 | 0/1
honest@! | aunest | 1/2 |  0 /1 | 0/2
art@! | old |  0 /4 | 0/4 | 1/4
honest@! | onest | 1/2 |  0 /1 | 0/2
listen@! | lesen | 1/2 |  0 /2 | 0/2
listen@! | listo | 1/2 |  0 /2 | 0/2
the@! same | thesaye | 1/1 |  0 /0 | 0/0
de@! | toe | 1/350 |  0 /359 | 0/343
lens@! | lances | 1/1 |  0 /1 | 0/1
pupil@! | puple | 1/3 |  0 /3 | 0/3
art@! | part |  0 /4 | 0/4 | 1/4
be@! | if |  0 /22 | 0/21 | 1/22
be@! | have |  0 /22 | 1/21 | 0/22
have@! no ball | havenowball he | 1/1 |  0 /0 | 0/0
crisp@! | crip | 1/9 |  0 /9 | 0/8
3rd@! | thir | 1/1 |  0 /1 | 0/1
crisp@! | please |  0 /9 | 1/9 | 0/8
crisp@! | chrisp |  0 /9 | 0/9 | 1/8
o’clock@! | a clock | 1/6 |  0 /6 | 0/5
crisp@! | c |  0 /9 | 0/9 | 1/8
crisp@! | kid |  0 /9 | 0/9 | 1/8
o’clock@! | clock |  0 /6 | 0/6 | 1/5
crisp@! | cris | 1/9 |  0 /9 | 0/8
o’clock@! | oak lor |  0 /6 | 0/6 | 1/5
3rd@! | dirt |  0 /1 | 1/1 | 0/1
stings@! | things | 1/1 |  0 /1 | 0/1
spieling@! | peeling |  0 /2 | 1/2 | 0/1
o’clock@! | oaklock |  0 /6 | 0/6 | 1/5
spieling@! | spealing | 1/2 |  0 /2 | 0/1
spieling@! | spiling | 1/2 |  0 /2 | 0/1
leaving@! | living |  0 /1 | 1/1 | 0/1
leaving@! | leving | 1/1 |  0 /1 | 0/1
animes@! | nimes |  0 /1 | 0/1 | 1/1
3rd@! | l turapode |  0 /1 | 0/1 | 1/1
o’clock@! | elo | 1/6 |  0 /6 | 0/5
giraff@! | giraffe |  0 /1 | 1/1 | 0/1
who@! with | home | 1/1 |  0 /0 | 0/0
math@! | maut | 1/10 |  0 /10 | 0/10
cheating@! | cheering |  0 /1 | 0/0 | 1/1
cheating@! | cheeving | 1/1 |  0 /0 | 0/1
quick@! | kuick | 1/1 |  0 /1 | 0/1
that@! | so |  0 /92 | 1/92 | 0/61
mosten@! | mosty | 1/1 |  0 /1 | 0/1
o’clock@! | okolck | 1/6 |  0 /6 | 0/5
sco@! | s | 1/1 |  0 /1 | 0/1
tell@! | tail |  0 /6 | 1/6 | 0/6
cannies@! | candis | 1/2 |  0 /2 | 0/2
tell@! | tells |  0 /6 | 1/6 | 0/6
tell@! | tailor |  0 /6 | 0/6 | 1/6
tell@! | houten i |  0 /6 | 0/6 | 1/6
tell@! | tair | 1/6 |  0 /6 | 0/6
tell@! | haten | 1/6 |  0 /6 | 0/6
cannies@! | canes |  0 /2 | 1/2 | 0/2
cannies@! | cammy |  0 /2 | 1/2 | 0/2
cannies@! | comes |  0 /2 | 0/2 | 1/2
animes@! | anms | 1/1 |  0 /1 | 0/1
he@! | h | 1/137 |  0 /134 | 0/132
he@! | as |  0 /137 | 0/134 | 1/132
he@! | her | 1/137 |  0 /134 | 0/132
kilo@! | ye |  0 /1 | 0/1 | 1/1
kilo@! | killo | 1/1 |  0 /1 | 0/1
snow beers@! | snowbears | 1/1 |  0 /0 | 0/0
beers@! | piers |  0 /7 | 1/7 | 0/8
beers@! | pope |  0 /7 | 0/7 | 1/8
beers@! | peers |  0 /7 | 0/7 | 1/8
he@! | thes | 1/137 |  0 /134 | 0/132
he@! | he’s | 1/137 |  0 /134 | 0/132
draw@! | drew |  0 /8 | 1/9 | 0/9
honey@! | holi | 1/3 |  0 /3 | 0/2
draw@! | dropped |  0 /8 | 0/9 | 1/9
draw@! | threw |  0 /8 | 0/9 | 1/9
draw@! | drove |  0 /8 | 0/9 | 1/9
draw@! | tra |  0 /8 | 0/9 | 1/9
he@! | ye | 1/137 |  0 /134 | 0/132
draw@! | drawt | 1/8 |  0 /9 | 0/9
draw@! | straww | 1/8 |  0 /9 | 0/9
he@! | hit | 1/137 |  0 /134 | 0/132
kilo@! | _ |  0 /1 | 1/1 | 0/1
honey@! | hon |  0 /3 | 0/3 | 1/2
which@! | wild |  0 /7 | 1/7 | 0/7
hose@! | _ |  0 /2 | 1/2 | 0/2
which@! | word |  0 /7 | 0/7 | 1/7
he@! | herh | 1/137 |  0 /134 | 0/132
which@! | woul | 1/7 |  0 /7 | 0/7
he@! | no | 1/137 |  0 /134 | 0/132
he@! | het | 1/137 |  0 /134 | 0/132
birsday@! | birth day |  0 /5 | 0/5 | 1/5
he@! | ha | 1/137 |  0 /134 | 0/132
ranger@! | rainsur | 1/1 |  0 /1 | 0/1
hose@! | hoe | 1/2 |  0 /2 | 0/2
honey@! | holding |  0 /3 | 1/3 | 0/2
hose@! | u | 1/2 |  0 /2 | 0/2
clowns@! | clouds |  0 /1 | 0/1 | 1/1
clowns@! | clounds | 1/1 |  0 /1 | 0/1
ne@! | the |  0 /1 | 1/1 | 0/1
ne@! | _ |  0 /1 | 0/1 | 1/1
ne@! | no | 1/1 |  0 /1 | 0/1
honey@! | ronja |  0 /3 | 1/3 | 0/2
honey@! | horny |  0 /3 | 1/3 | 0/2
it’s for@! | 0 | 1/1 |  0 /0 | 0/0
vanille@! | anil |  0 /7 | 0/8 | 1/6
floor@! | flu |  0 /1 | 0/1 | 1/1
vanille@! | vanel |  0 /7 | 0/8 | 1/6
will@! | wiu | 1/109 |  0 /108 | 0/109
we@! | weth | 1/20 |  0 /22 | 0/21
we@! | astda | 1/20 |  0 /22 | 0/21
left@! | leftu | 1/3 |  0 /3 | 0/3
left@! | the | 1/3 |  0 /3 | 0/3
and@! eh and | my wal | 1/1 |  0 /0 | 0/0
the@! airplane | day are playing | 1/1 |  0 /0 | 0/0
pool@! | _ |  0 /1 | 1/1 | 0/1
pool@! | pone | 1/1 |  0 /1 | 0/1
fly in a@! | flying | 1/1 |  0 /0 | 0/0
need@! | neat |  0 /24 | 0/24 | 1/24
will@! | wl | 1/109 |  0 /108 | 0/109
need@! | got |  0 /24 | 0/24 | 1/24
will@! | wa | 1/109 |  0 /108 | 0/109
need@! | t | 1/24 |  0 /24 | 0/24
need@! | t e | 1/24 |  0 /24 | 0/24
will@! | weal | 1/109 |  0 /108 | 0/109
freets@! | frets |  0 /1 | 0/1 | 1/1
will@! | wouldit | 1/109 |  0 /108 | 0/109
we@! | re |  0 /20 | 0/22 | 1/21
we@! | we’re |  0 /20 | 1/22 | 0/21
we@! | we’ve |  0 /20 | 1/22 | 0/21
other@! | and all |  0 /14 | 1/11 | 0/12
of@! things | with | 1/1 |  0 /0 | 0/0
auch@! | because | 1/1 |  0 /0 | 0/0
that@! is | to scy | 1/1 |  0 /0 | 0/0
i@! can | something at | 1/1 |  0 /0 | 0/0
i see@! | this | 1/1 |  0 /0 | 0/0
lands@! | last |  0 /3 | 1/3 | 0/3
lands@! | _ |  0 /3 | 0/3 | 1/3
lands@! | and | 1/3 |  0 /3 | 0/3
other@! | adorn |  0 /14 | 0/11 | 1/12
we@! | are |  0 /20 | 1/22 | 0/21
other@! | are |  0 /14 | 0/11 | 1/12
other@! | plane |  0 /14 | 0/11 | 1/12
will@! | vou | 1/109 |  0 /108 | 0/109
will@! | where | 1/109 |  0 /108 | 0/109
other@! | athe | 1/14 |  0 /11 | 0/12
will@! | woulld | 1/109 |  0 /108 | 0/109
we@! i need | win meet | 1/1 |  0 /0 | 0/0
tower@! victor@g | loveitt | 1/1 |  0 /0 | 0/0
products@! | prodocs | 1/1 |  0 /1 | 0/1
will@! | wel | 1/109 |  0 /108 | 0/109
1000000s@! | mollie |  0 /2 | 0/1 | 1/2
correctly@! | acorrectly | 1/2 |  0 /2 | 0/2
will@! | rod |  0 /109 | 0/108 | 1/109
violence@! | violance | 1/1 |  0 /1 | 0/1
and him@! | team |  0 /1 | 0/0 | 1/1
and him@! | oni | 1/1 |  0 /0 | 0/1
lounge@! | loins |  0 /1 | 0/1 | 1/1
lounge@! | launs | 1/1 |  0 /1 | 0/1
pillow@! | bureau |  0 /3 | 0/3 | 1/3
pillow@! | below | 1/3 |  0 /3 | 0/3
rosaliewater@! | i’m |  0 /1 | 1/1 | 0/1
rose@! | aros | 1/2 |  0 /3 | 0/3
rosaliewater@! | grizzle in water |  0 /1 | 0/1 | 1/1
rosaliewater@! | hruseli water | 1/1 |  0 /1 | 0/1
brit englander@! | a pruh inilender | 1/1 |  0 /0 | 0/0
will@! | willn’t |  0 /109 | 0/108 | 1/109
will@! | with |  0 /109 | 0/108 | 1/109
will@! | well |  0 /109 | 0/108 | 1/109
pain@! | paine | 1/1 |  0 /1 | 0/1
floor@! | flowers |  0 /1 | 1/1 | 0/1
ast@! | east |  0 /1 | 1/1 | 0/1
rose@! | rows |  0 /2 | 0/3 | 1/3
1000000s@! | mornen | 1/2 |  0 /1 | 0/2
will@! | we’d |  0 /109 | 0/108 | 1/109
1000000s@! | 100000000s | 1/2 |  0 /1 | 0/2
bend@! butter | bendbutter | 1/1 |  0 /0 | 0/0
cack@! | kak |  0 /1 | 0/1 | 1/1
cack@! | kack | 1/1 |  0 /1 | 0/1
pee@! | pp |  0 /3 | 0/2 | 1/2
will@! | woull | 1/109 |  0 /108 | 0/109
will@! | or |  0 /109 | 0/108 | 1/109
will@! | had |  0 /109 | 0/108 | 1/109
will@! | real |  0 /109 | 0/108 | 1/109
rose@! | arrows |  0 /2 | 1/3 | 0/3
0 gravity@! | zerogravity |  0 /1 | 1/1 | 0/0
0 gravity@! | scerocrafd | 1/1 |  0 /1 | 0/0
wish@! | wishs | 1/2 |  0 /2 | 0/2
will@! | did |  0 /109 | 0/108 | 1/109
magazin@! | margetcin | 1/1 |  0 /1 | 0/1
magi@! | muggie |  0 /1 | 1/1 | 0/1
magi@! | mad | 1/1 |  0 /1 | 0/1
rose@! | rolls |  0 /2 | 1/3 | 0/3
make@! some@! | ianpos | 1/1 |  0 /0 | 0/0
yeah one@! day | yeahoday | 1/1 |  0 /0 | 0/0
wake@! | wau | 1/2 |  0 /1 | 0/1
got@! | sgot | 1/21 |  0 /21 | 0/21
infest@! | and fast |  0 /1 | 1/1 | 0/0
infest@! | infast | 1/1 |  0 /1 | 0/0
gonna@! | going a |  0 /4 | 0/4 | 1/4
gonna@! | ong | 1/4 |  0 /4 | 0/4
instead@! | in send |  0 /2 | 0/2 | 1/2
instead@! | in seat |  0 /2 | 0/2 | 1/2
instead@! | incent | 1/2 |  0 /2 | 0/2
instead@! | i se | 1/2 |  0 /2 | 0/2
wilt@! | would |  0 /1 | 1/1 | 0/1
wandering@! | wondring | 1/2 |  0 /2 | 0/1
wilt@! | will |  0 /1 | 0/1 | 1/1
wilt@! | wil | 1/1 |  0 /1 | 0/1
got@! | gotit | 1/21 |  0 /21 | 0/21
families@! | _ |  0 /3 | 1/3 | 0/3
got@! | scott |  0 /21 | 0/21 | 1/21
families@! | familys | 1/3 |  0 /3 | 0/3
swept@! | swepd | 1/2 |  0 /2 | 0/2
swept@! | sweapt | 1/2 |  0 /2 | 0/2
further@! | forther | 1/1 |  0 /1 | 0/1
wandering@! | wndering u | 1/2 |  0 /2 | 0/1
prom@! | prone |  0 /2 | 0/2 | 1/2
vanille@! | when | 1/7 |  0 /8 | 0/6
vanille@! | wendel |  0 /7 | 0/8 | 1/6
vanille@! | wail |  0 /7 | 0/8 | 1/6
vanille@! | one el |  0 /7 | 0/8 | 1/6
vanille@! | onene | 1/7 |  0 /8 | 0/6
vanille@! | vanil | 1/7 |  0 /8 | 0/6
vanille@! | vani | 1/7 |  0 /8 | 0/6
vanille@! | vannil | 1/7 |  0 /8 | 0/6
vanille@! | vone il | 1/7 |  0 /8 | 0/6
vanille@! | vunni | 1/7 |  0 /8 | 0/6
wandering@! | wondering |  0 /2 | 1/2 | 0/1
it or no@! | the tirno | 1/1 |  0 /0 | 0/0
pizzas@! | pitas | 1/1 |  0 /1 | 0/1
mark@! designing | marktes dininga | 1/1 |  0 /0 | 0/0
me@! friend | mefriend | 1/1 |  0 /0 | 0/0
falled@! | fallen |  0 /1 | 1/1 | 0/1
falled@! | followed |  0 /1 | 0/1 | 1/1
falled@! | fad | 1/1 |  0 /1 | 0/1
wandering@! | for |  0 /2 | 1/2 | 0/1
prom@! | _ |  0 /2 | 0/2 | 1/2
prom@! | ronn | 1/2 |  0 /2 | 0/2
aufstehen@! | i ove austein | 1/1 |  0 /0 | 0/0
buggies@! | bubgies | 1/1 |  0 /1 | 0/1
leaves@! | levs | 1/1 |  0 /1 | 0/1
will@! | wol | 1/109 |  0 /108 | 0/109
you’ll@! | youu | 1/1 |  0 /1 | 0/1
when@! | don’t |  0 /28 | 1/28 | 0/29
when@! | and |  0 /28 | 0/28 | 1/29
when@! | whend | 1/28 |  0 /28 | 0/29
dragging@! | draging | 1/1 |  0 /1 | 0/1
phoned@! | phond | 1/1 |  0 /1 | 0/1
sleep long@! | sleepalong | 1/1 |  0 /0 | 0/0
questions@! | guestions | 1/5 |  0 /5 | 0/5
will@! | hod | 1/109 |  0 /108 | 0/109
will@! | willd | 1/109 |  0 /108 | 0/109
will@! | wi | 1/109 |  0 /108 | 0/109
a chocolate@! please | chocolatet plea | 1/1 |  0 /0 | 0/0
is is@! | isis | 1/1 |  0 /0 | 0/0
switzer@! | switer | 1/1 |  0 /1 | 0/1
will@! | ris | 1/109 |  0 /108 | 0/109
dinosour@! | dinosa | 1/1 |  0 /1 | 0/1
up@! go@! | upgo | 1/1 |  0 /0 | 0/0
someting@! | something |  0 /1 | 1/1 | 0/1
prom@! | pron | 1/2 |  0 /2 | 0/2
sunglass@! | fernglas | 1/3 |  0 /3 | 0/3
should@! | so |  0 /2 | 0/2 | 1/2
should@! | sud | 1/2 |  0 /2 | 0/2
sunglass@! | glass |  0 /3 | 1/3 | 0/3
sunglass@! | fingers |  0 /3 | 1/3 | 0/3
sunglass@! | last |  0 /3 | 0/3 | 1/3
sunglass@! | ferndale |  0 /3 | 0/3 | 1/3
sunglass@! | esson class |  0 /3 | 0/3 | 1/3
sunglass@! | las | 1/3 |  0 /3 | 0/3
these@! | them |  0 /4 | 1/5 | 0/5
stinkbear@! | stingbe | 1/1 |  0 /1 | 0/1
someting@! | or ta | 1/1 |  0 /1 | 0/1
someting@! | of tape |  0 /1 | 0/1 | 1/1
it’s@! bites | it spids | 1/1 |  0 /0 | 0/0
cover@! | _ |  0 /1 | 1/1 | 0/1
cover@! | covering |  0 /1 | 0/1 | 1/1
cover@! | covur | 1/1 |  0 /1 | 0/1
stinkbear@! | _ |  0 /1 | 1/1 | 0/1
stinkbear@! | think |  0 /1 | 0/1 | 1/1
s@! | _ |  0 /0 | 1/1 | 0/0