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Automatic Phonetic Segmentation Using the Kaldi Toolkit

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Book cover Text, Speech, and Dialogue (TSD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10415))

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Abstract

In this paper we explore the possibilities of hidden Markov model based automatic phonetic segmentation with the Kaldi toolkit. We compare the Kaldi toolkit and the Hidden Markov Model Toolkit (HTK) in terms of segmentation accuracy. The well-tuned HTK-based phonetic segmentation framework was taken as the baseline and compared to a newly proposed segmentation framework built from the default examples and recipes available in the Kaldi repository. Since the segmentation accuracy of the HTK-based system was significantly higher than that of the Kaldi-based system, the default Kaldi setting was modified with respect to pause model topology, the way of generating phonetic questions for clustering, and the number of Gaussian mixtures used during modeling. The modified Kaldi-based system achieved results comparable to those obtained by HTK—slightly worse for small segmentation errors but better for gross segmentation errors. We also confirmed that, for both toolkits, the standard three-state left-to-right model topology was significantly outperformed by a modified five-state left-to-right topology, especially with respect to small segmentation errors.

This research was supported by the Technology Agency of the Czech Republic (TA CR), project No. TH02010307.

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Authors and Affiliations

  1. Department of Cybernetics, Faculty of Applied Sciences, New Technology for the Information Society (NTIS), University of West Bohemia, Plzeň, Czech Republic

    Jindřich Matoušek & Michal Klíma

Authors
  1. Jindřich Matoušek
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  2. Michal Klíma
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Correspondence to Jindřich Matoušek .

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Editors and Affiliations

  1. University of West Bohemia, Pilsen, Czech Republic

    Kamil Ekštein

  2. University of West Bohemia, Pilsen, Czech Republic

    Václav Matoušek

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Matoušek, J., Klíma, M. (2017). Automatic Phonetic Segmentation Using the Kaldi Toolkit. In: Ekštein, K., Matoušek, V. (eds) Text, Speech, and Dialogue. TSD 2017. Lecture Notes in Computer Science(), vol 10415. Springer, Cham. https://doi.org/10.1007/978-3-319-64206-2_16

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  • DOI: https://doi.org/10.1007/978-3-319-64206-2_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64205-5

  • Online ISBN: 978-3-319-64206-2

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