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

T.J. Viering Computer Science & Engineering-Teaching Team - EEMCS (mentor)
M. Loog Pattern Recognition and Bioinformatics - EEMCS (coach)
S. Makrodimitris Pattern Recognition and Bioinformatics - EEMCS (mentor)
A. Naseri Jahfari Pattern Recognition and Bioinformatics - EEMCS (mentor)
Catharine Oertel Swiss Federal Institute of Technology (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.