Foreign Accent Detection In Air Traffic Control Communication
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2020-07-01
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en
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Abstract
Miscommunication in the world of pilots and ATC’s is a reoccurring problem
that causes terrible accidents when happening at a critical moment. Such miscommunications
can arise due to the difference in nationality between the communicators
due to varying pronunciations. The goal of this thesis was to create a deep
neural network that would be able to determine the native language of a pilot by
analysing their English speech. The languages that I have focussed on for this research
are Dutch, German, and French. In order to achieve the goal, I have created
a 2-dimensional Convolutional Neural Network which I fed with 1, 2, and 3 second
long audio segments of pilots talking. I have tested this model on several performance
measures: accuracy, loss, precision, recall and f1-score. After training the
model over 20 epochs, the 1, 2, and 3 second-long segments ended up having an
accuracy of 49%, 47%, and 45%. And after having performed data augmentation on
the data in an attempt to reduce the overfitting problem, the 1, 2, and 3 second-long
segments ended up having an accuracy of 53%, 54%, and 56%. A great improvement
but still overfitting occurred. And so the results have shown that to a certain extent,
it is indeed possible to predict the native accent of a pilot. For the future research
this would mean that it is recommended to work on the prevention of overfitting,
for example by using a much bigger dataset than the one that was used in this research.
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Faculteit der Sociale Wetenschappen