[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

Using Part of Speech N-Grams for Improving Automatic Speech Recognition of Polish

  • Conference paper
Machine Learning and Data Mining in Pattern Recognition (MLDM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7988))

Abstract

This paper investigates the usefulness of a part of speech language model on the task of automatic speech recognition. The develped model uses part of speech tags as categories in a category-based language model. The constructed model is used to re-score the hypotheses generated by the HTK acoustic module. The probability of a given sequence of words is estimated using n-grams with Witten-Bell backoff.

The experiments presented in this paper were carried out for Polish. The best obtained results show that the part-of-speech-only language model trained on a 1-million manually tagged corpus reduces the word error rate by more than 10 percentage points.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 35.99
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 44.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Ziółko, B., Skurzok, D.: N-grams model for Polish. Speech and Language Technologies, Book 2, pp. 107–127. InTech Publisher (2011)

    Google Scholar 

  2. Jurafsky, D., Martin, J.H.: Speech and Language Processing, 2nd edn. Prentice-Hall, Inc., New Jersey (2008)

    Google Scholar 

  3. Hirsimaki, T., Pylkkonen, J., Kurimo, M.: Importance of high-order n-gram models in morph-based speech recognition. IEEE Transactions on Audio, Speech and Language Processing 17(4), 724–732 (2009)

    Article  Google Scholar 

  4. Sak, H., Saraçlar, M., Gungor, T.: Morpholexical and discriminative language models for turkish automatic speech recognition. IEEE Transactions on Audio, Speech, and Language Processing 20(8), 2341–2351 (2012)

    Article  Google Scholar 

  5. Szałkiewicz, Ł., Przepiórkowski, A.: Anotacja morfoskładniowa. In: Narodowy Korpus Języka Polskiego, pp. 59–96. Wydawnictwo Naukowe PWN (2012)

    Google Scholar 

  6. Radziszewski, A.: A tiered CRF tagger for polish. In: Bembenik, R., Skonieczny, Ł., Rybiński, H., Kryszkiewicz, M., Niezgódka, M. (eds.) Intelligent Tools for Building a Scientific Information Platform. SCI, vol. 467, pp. 215–230. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  7. Niesler, T., Whittaker, E., Woodland, P.: Comparison of part-of-speech and automatically derived category-based language models for speech recognition. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 1, pp. 177–180. IEEE (1998)

    Google Scholar 

  8. Ziółko, B., Manandhar, S., Wilson, R.C., Ziółko, M.: Language model based on pos tagger. In: Proceedings of SIGMAP 2008 the International Conference on Signal Processing and Multimedia Applications, Porto (2008)

    Google Scholar 

  9. Piasecki, M.: Hand-written and automatically extracted rules for polish tagger. In: Sojka, P., Kopeček, I., Pala, K. (eds.) TSD 2006. LNCS (LNAI), vol. 4188, pp. 205–212. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Burnard, L., Sperberg-McQueen, C.: Guidelines for electronic text encoding and interchange. In: Association for Computers and the Humanities, Association for Computational Linguistics, Association for Literary and Linguistic Computing (1994)

    Google Scholar 

  11. Przepiórkowski, A.: Korpus IPI PAN. Wersja wstępna. Instytut Podstaw Informatyki PAN (2004)

    Google Scholar 

  12. Janus, D., Przepiórkowski, A.: Poliqarp 1.0: Some technical aspects of a linguistic search engine for large corpora. In: The Proceedings of Practical Applications of Linguistic Corpora (2005)

    Google Scholar 

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

    Google Scholar 

  14. Saloni, Z., Woliński, M., Wołosz, R., Gruszczyński, W., Skowrońska, D.: Słownik gramatyczny języka polskiego (Eng. Grammatical dictionary of Polish) (2102)

    Google Scholar 

  15. Radziszewski, A., Śniatowski, T.: A memory-based tagger for polish. In: Proceedings of the 5th Language & Technology Conference, Poznań (2011)

    Google Scholar 

  16. Acedański, S.: A morphosyntactic brill tagger for inflectional languages. In: Loftsson, H., Rögnvaldsson, E., Helgadóttir, S. (eds.) IceTAL 2010. LNCS, vol. 6233, pp. 3–14. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  17. Young, S.: Large vocabulary continuous speech recognition: a review. IEEE Signal Processing Magazine 13(5), 45–57 (1996)

    Article  Google Scholar 

  18. Young, S., Evermann, G., Gales, M., Hain, T., Kershaw, D., Moore, G., Odell, J., Ollason, D., Povey, D., Valtchev, V., Woodland, P.: HTK Book. Cambridge University Engineering Department, UK (2005)

    Google Scholar 

  19. Grocholewski, S.: CORPORA - speech database for Polish diphones. In: Proceedings of Eurospeech (1997)

    Google Scholar 

  20. Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. In: Proceedings of the 34th Annual Meeting on Association for Computational Linguistics, pp. 310–318. Association for Computational Linguistics (1996)

    Google Scholar 

  21. Jurafsky, D., Martin, J., Kehler, A.: Speech and language processing: An introduction to natural language processing, computational linguistics, and speech recognition, 2nd edn. Prentice Hall (2009)

    Google Scholar 

  22. Kneser, R., Ney, H.: Improved backing-off for m-gram language modeling. In: 1995 International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1995, vol. 1, pp. 181–184. IEEE (1995)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Pohl, A., Ziółko, B. (2013). Using Part of Speech N-Grams for Improving Automatic Speech Recognition of Polish. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2013. Lecture Notes in Computer Science(), vol 7988. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39712-7_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39712-7_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39711-0

  • Online ISBN: 978-3-642-39712-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics