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A Comparative Analysis of Speech Recognition Systems for the Tatar Language

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Book cover Computational Linguistics and Intelligent Text Processing (CICLing 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10761))

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Abstract

This paper presents a comparative study of several different approaches to speech recognition for the Tatar language. All the compared systems use a corpus-based approach, so recent results in speech and text corpora creation are also shown. The recognition systems differ in acoustic modelling algorithms, basic acoustic units, and language modelling techniques. The DNN-based system shows the best recognition result obtained on the test part of speech corpus.

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References

  1. Lewis, M.P., Simons, G.F., Fennig, C.D. (eds.). Ethnologue: Languages of the World, 9th (edn.). SIL International, Dallas (2016). http://www.ethnologue.com

  2. Berment, V.: “Me´thodes pour informatiser des langues et des groups de langues peu dotées”, Ph.D. thesis, J. Fourier University, Grenoble I (2004)

    Google Scholar 

  3. Krauwer, S.: The basic language resource kit (BLARK) as the first milestone for the language resources roadmap. In: Proceedings of International Workshop Speech and Computer SPEECOM, Moscow, Russia, pp. 8–15 (2003)

    Google Scholar 

  4. Khusainov, A.: Tekhnologiya avtomatizatsii sozdaniya I otsenki kachestva programmnikh sredstv analiza rechi c uchetom osobennostey maloresursnykh yazikov, Ph.D. thesis, Kazan, 162 p (2014)

    Google Scholar 

  5. Salimzyanov, I., Washington, J., Tyers, F.: A free/open-source Kazakh-Tatar machine translation system. In: Proceedings of the Machine Translation Summit XIV, Nice, France (2013)

    Google Scholar 

  6. Yandex Translate. https://translate.yandex.com/translator/Russian-Tatar

  7. Suleymnov, D., Gatiatullin, A., Gilmullin, R.: Lexicograficheskaya baza dannykh dlya system mashinnogo perevoda blizkorodstvennykh yazykov. In: Proceedings of Third International Conference «Informatizatciya obschestva», Astana, Kazakhstan, pp. 585–587 (2012)

    Google Scholar 

  8. Khusainov, A., Khusainova, A.: Speech human-machine interface for the Tatar language. In: Artificial Intelligence and Natural Language Conference, FRUCT Oy, Helsinki, pp. 60–65 (2016)

    Google Scholar 

  9. Khusainov, A., Suleymanov, D.: Language identification system for the tatar language. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) SPECOM 2013. LNCS (LNAI), vol. 8113, pp. 203–210. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-01931-4_27

    Chapter  Google Scholar 

  10. Suleymanov, Dz., Nevzorova, O.A., Khakimov, B.: National corpus of the tatar language “Tugan Tel”: structure and features of grammatical annotation. In: Proceedings of International Conference Georgian Language and modern Technology, Tbilisi, pp. 107–108 (2013)

    Google Scholar 

  11. Povey, D., et al.: The kaldi speech recognition toolkit. In: Proceedings of ASRU, pp. 1–4 (2011)

    Google Scholar 

  12. Rath, S.P., Povey, D., Vesely, K., Cernocky, J.H.: Improved feature processing for deep neural networks. In: Proceedings of InterSpeech (2013)

    Google Scholar 

  13. Zhang, X., Trmal, J., Povey, D., Khudanpur, S.: Improving deep neural network acoustic models using generalized maxout networks. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2014, pp. 215–219. IEEE (2014)

    Google Scholar 

  14. Stolcke, A.: SRILM – an extensible language modeling toolkit. In: Proceedings of International Conference on Spoken Language Processing, vol. 2, Denver, pp. 901–904 (2002)

    Google Scholar 

  15. Stolcke, A.: Entropy-based pruning of backoff language models. In: Proceedings of DARPA Broadcast News Transcription and Understanding Workshop, Lansdowne, pp. 270–274 (1998)

    Google Scholar 

  16. Kneser, R., Ney, H.: Improved backingoff for m-gram language modeling. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1 (1995)

    Google Scholar 

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Authors and Affiliations

  1. Institute of Applied Semiotics of the Tatarstan Academy of Sciences, Kazan Federal University, Kazan, Russia

    Aidar Khusainov

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  1. Aidar Khusainov
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Correspondence to Aidar Khusainov .

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Editors and Affiliations

  1. CIC, Instituto Politécnico Nacional, Mexico City, Mexico

    Alexander Gelbukh

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Khusainov, A. (2018). A Comparative Analysis of Speech Recognition Systems for the Tatar Language. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10761. Springer, Cham. https://doi.org/10.1007/978-3-319-77113-7_40

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  • DOI: https://doi.org/10.1007/978-3-319-77113-7_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77112-0

  • Online ISBN: 978-3-319-77113-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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