It sounds like Greek to me

Performance of phonetic representations for language identification

Bachelor thesis (2021)

Authors

D.J. IJpma Electrical Engineering, Mathematics and Computer Science

Contributors

Tom Julian Viering Computer Science & Engineering-Teaching Team (mentor)
M. Loog Pattern Recognition and Bioinformatics (coach)
S. Makrodimitris Pattern Recognition and Bioinformatics (mentor)
Arman Naseri Pattern Recognition and Bioinformatics (mentor)
Catharine Oertel École Polytechnique Fédérale de Lausanne (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Johannes IJpma
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Published Date

01-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

This paper compares the performance of two phonetic notations, IPA and ASJPcode, with the alphabetical notation for word-level language identification. Two machine learning models, a Multilayer Percerptron and a Logistic Regression model, are used to classify words using each of the three notations. With both models the IPA notation outperforms the other two notations.

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