0000000001033114

AUTHOR

Ansis Ataols Bērziņš

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

2019

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 …

Space (punctuation)Kullback–Leibler divergenceLanguage identificationSimilarity (network science)Computer scienceSpeech recognitionComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)Hidden Markov modelUSableDivergence (statistics)
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