Comparison of Linguistic Language Classification based on Origin and Data Driven Language Classification using the IPA and Clustering

Bachelor thesis (2021)

Authors

I.G.M. Rethans Electrical Engineering, Mathematics and Computer Science

Contributors

T.J. Viering (mentor)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Isa Rethans
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Published Date

01-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Language similarity is very useful for enrichment data in both Natural Lanuguage Processing (NLP) and Automatic Speech Recognition (ASR). A clustering algorithm could provide an efficient means to define language similarity in a data-driven way. This research investigates the relation between linguistic classification by origin and data driven classification based on the pronunciation of languages using k-means clustering where the focus is placed
on the Indo-European languages. The results show large variation in cluster results and consequently large variation in correspondence with linguistic
classification. This is caused by a relatively even spread of the data over the feature space. Still, the results indicate significance in the relation between
the two classification methods. Furthermore, this research functions as a foundation and a source of inspiration for a lot of possible future research.

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