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International Conference on Text, Speech, and Dialogue

TSD 2017: Text, Speech, and Dialogue pp 174–182Cite as

Parliament Archives Used for Automatic Training of Multi-lingual Automatic Speech Recognition Systems

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

Abstract

In the paper we present a fully automated process capable of creating speech databases needed for training acoustic models for speech recognition systems. We show that archives of national parliaments are perfect sources of speech and text data suited for a lightly supervised training scheme, which does not require human intervention. We describe the process and its procedures in details and demonstrate its usage on three Slavic languages (Polish, Russian and Bulgarian). Practical evaluation is done on a broadcast news task and yields better results than those obtained on some established speech databases.

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Notes

  1. 1.

    http://www.sejm.gov.pl/.

  2. 2.

    http://www.duma.gov.ru/.

  3. 3.

    http://www.parliament.bg/tv/.

  4. 4.

    https://gitlab.ite.tul.cz/SpeechLab/EastSlavicTestData.

  5. 5.

    https://gitlab.ite.tul.cz/SpeechLab/SouthSlavicTestData.

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Acknowledgements

The research was supported by the Technology Agency of the Czech Republic (project TA04010199) and by the Student Grant Scheme at the Technical University of Liberec.

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Authors and Affiliations

  1. Institute of Information Technology and Electronics, Technical University of Liberec, Studentska 2, 461 17, Liberec, Czech Republic

    Jan Nouza & Radek Safarik

Authors
  1. Jan Nouza
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  2. Radek Safarik
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Correspondence to Jan Nouza .

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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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Nouza, J., Safarik, R. (2017). Parliament Archives Used for Automatic Training of Multi-lingual Automatic Speech Recognition Systems. 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_20

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  • DOI: https://doi.org/10.1007/978-3-319-64206-2_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64205-5

  • Online ISBN: 978-3-319-64206-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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