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Conference on Artificial Intelligence and Natural Language

AINL 2019: Artificial Intelligence and Natural Language pp 113–125Cite as

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

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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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Fig. 1.

Notes

  1. 1.

    We chose women because we collected more female voice speech data in our expeditions – apparently because women live longer [12] and are more talkative (at least by our observations, although in research their predominance of daily word use does not meet thresholds for statistical significance, e.g., [13, 14]).

  2. 2.

    This program is used to perform a single re-estimation of the parameters of a set of HMMs using an embedded training version of the Baum-Welch algorithm. Training data consists of one or more utterances each of which has a transcription in the form of a standard label file (segment boundaries are ignored). For each training utterance, a composite model is effectively synthesized by concatenating the phoneme models given by the transcription. [5]

  3. 3.

    We are also concerned with the statistical problem of discrimination, by considering a measure of "distance" or "divergence" between statistical populations in terms of our measure of information. For the statistician two populations differ more or less according as to how difficult it is to discriminate between them with the best test. The particular measure we use has been considered by Jeffreys in another connection. He is primarily concerned with its use in providing an invariant density of a priory probability. A special case of this divergence is Mahalanobis' generalized distance. [6]

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Authors and Affiliations

  1. University of Latvia, Maskavas 54, Riga, LV-1003, Latvia

    Ansis Ataols Bērziņš

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Correspondence to Ansis Ataols Bērziņš .

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Editors and Affiliations

  1. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Dmitry Ustalov

  2. ITMO University, St. Petersburg, Russia

    Andrey Filchenkov

  3. Computer Science, University of Helsinki, Helsinki, Finland

    Lidia Pivovarova

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Bērziņš, A.A. (2019). Usage of HMM-Based Speech Recognition Methods for Automated Determination of a Similarity Level Between Languages. In: Ustalov, D., Filchenkov, A., Pivovarova, L. (eds) Artificial Intelligence and Natural Language. AINL 2019. Communications in Computer and Information Science, vol 1119. Springer, Cham. https://doi.org/10.1007/978-3-030-34518-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-34518-1_8

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