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Usage of HMM-Based Speech Recognition Methods for Automated Determination of a Similarity Level Between Languages

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1119)

Abstract

The problem of automated determination of language similarity (or even defining of a distance on the space of languages) could be solved in different ways – working with phonetic transcriptions, with speech recordings or both of them. For the recordings, we propose and test a HMM-based one: in the first part of our article we successfully try language detection, afterwards we are trying to calculate distances between HMM-based models, using different metrics and divergences. The Kullback-Leibler divergence is the only one we got good results with – it means that the calculated distances between languages correspond to analytical understanding of similarity between them. Even if it does not work very well, the conclusion is that this method is usable, but usage of some other methods could be more rational.

Keywords

Distance between languages Hidden Markov models Kullback-Leibler divergence 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of LatviaRigaLatvia

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