Skip to main content

On the Use of Machine Learning and Syllable Information in European Portuguese Grapheme-Phone Conversion

  • Conference paper
Computational Processing of the Portuguese Language (PROPOR 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3960))

Abstract

In this study evaluation of two self-learning methods (MBL and TBL) on European Portuguese grapheme-to-phone conversion is presented. Combinations (parallel and cascade) of the two systems were also tested. The usefulness of syllable information is also investigated. Systems with good performance were obtained both using a single self-learning method and combinations. Best performance was obtained with MBL and the parallel combination. The use of syllable information contributes to a better performance in all systems tested.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • 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. Marchand, Y., Damper, R.: Can syllabification improve pronunciation by analogy of english? Natural Language Engineering 1(1), 1–25 (2005)

    Google Scholar 

  2. Reichel, U.D., Schiel, F.: Using morphology and phoneme history to improve grapheme-tophoneme conversion. In: Proc. InterSpeech Lisboa (2005)

    Google Scholar 

  3. Oliveira, C., Moutinho, L.C., Teixeira, A.: On European Portuguese automatic syllabification. In: Proc. InterSpeech Lisboa (2005)

    Google Scholar 

  4. Brill, E.: Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging. Computational Linguistics 21, 543–565 (1995)

    Google Scholar 

  5. Florian, R., Ngai, G.: Fast Transformation-Based Learning (2001)

    Google Scholar 

  6. Daelemans, W., Zavrel, J., van der Sloot, K., van den Bosch, A.: TiMBL: Tilburg memory based learner, version 5.1, reference guide. Reference Guide ILK-0402, Tilburg University, Reference: ILK Research Group Technical Report Series no. 04-02 (2004)

    Google Scholar 

  7. Nascimento, F., Marques, L., Segura, L.: Português fundamental: Métodos e documentos. Technical report, INIC-CLUL, Lisboa (1987)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Teixeira, A., Oliveira, C., Moutinho, L. (2006). On the Use of Machine Learning and Syllable Information in European Portuguese Grapheme-Phone Conversion. In: Vieira, R., Quaresma, P., Nunes, M.d.G.V., Mamede, N.J., Oliveira, C., Dias, M.C. (eds) Computational Processing of the Portuguese Language. PROPOR 2006. Lecture Notes in Computer Science(), vol 3960. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751984_24

Download citation

  • DOI: https://doi.org/10.1007/11751984_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34045-4

  • Online ISBN: 978-3-540-34046-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics