Skip to main content
SpringerLink
Log in

Helping Domain Experts Build Phrasal Speech Translation Systems

  • Conference paper
  • First Online:
Future and Emergent Trends in Language Technology (FETLT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9577))

Abstract

We present a new platform, “Regulus Lite”, which supports rapid development and web deployment of several types of phrasal speech translation systems using a minimal formalism. A distinguishing feature is that most development work can be performed directly by domain experts. We motivate the need for platforms of this type and discuss three specific cases: medical speech translation, speech-to-sign-language translation and voice questionnaires. We briefly describe initial experiences in developing practical systems.

Medical translation work was supported by Geneva University’s Innogap program. Work on sign language translation was supported by the Crédit Suisse, Raiffeisen, TeamCO and Max Bircher Foundations. We thank Nuance Inc. and the University of East Anglia for generously allowing us to use their software for research purposes.

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

Access this chapter

Log in via an institution
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 EPUB and 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

Notes

  1. 1.

    http://medibabble.com/.

  2. 2.

    The notation has been changed slightly for expositional purposes.

  3. 3.

    Hôpitaux Universitaires de Genéve.

  4. 4.

    Tigrinya will be added soon.

  5. 5.

    The largest parallel corpus used in sign language translation that we know of has about 8 700 utterances [11].

  6. 6.

    This is a slight oversimplification; in actual fact, recognition passes an n-best hypothesis list. The complications this introduces are irrelevant in the present context.

  7. 7.

    One subject misunderstood the instructions, one had severe audio problems with their connection, and a few utterances were spoiled by incorrect use of the push-to-talk interface.

References

  1. Armando, A., Bouillon, P., Rayner, E., Tsourakis, N.: A tool for building multilingual voice questionnaires. In: Proceedings of Translating and the Computer 36, London, England (2014)

    Google Scholar 

  2. Bouillon, P., et al.: Many-to-many multilingual medical speech translation on a PDA. In: Proceedings of the Eighth Conference of the Association for Machine Translation in the Americas, Waikiki, Hawaii (2008)

    Google Scholar 

  3. Braffort, A., Bossard, B., Segouat, J., Bolot, L., Lejeune, F.: Modélisation des relations spatiales en langue des signes française. Atelier LS, TALN, Dourdan, pp. 6–10 (2005)

    Google Scholar 

  4. Chang, D., Thyer, I., Hayne, D., Katz, D.: Using mobile technology to overcome language barriers in medicine. Ann. R. Coll. Surg. Engl. 96(6), e23–e25 (2014)

    Article  Google Scholar 

  5. Cox, S., Lincoln, M., Tryggvason, J., Nakisa, M., Wells, M., Tutt, M., Abbott, S.: Tessa, a system to aid communication with deaf people. In: Proceedings of the Fifth International ACM Conference on Assistive Technologies, pp. 205–212. ACM (2002)

    Google Scholar 

  6. Crasborn, O.: Nonmanual structures in sign language. In: Brown, K. (ed.) Encyclopedia of Language and Linguistics, vol. 8, 2nd edn, pp. 668–672. Elsevier, Oxford (2006)

    Chapter  Google Scholar 

  7. Ebling, S., Glauert, J.: Exploiting the full potential of JASigning to build an avatar signing train announcements. In: Proceedings of the Third International Symposium on Sign Language Translation and Avatar Technology (SLTAT), Chicago, USA, October 2013, vol. 18, p. 19 (2013)

    Google Scholar 

  8. Ebling, S., Huenerfauth, M.: Bridging the gap between sign language machine translation and sign language animation using sequence classification. In: Proceedings of the 6th Workshop on Speech and Language Processing for Assistive Technologies (SLPAT) (2015)

    Google Scholar 

  9. Elliott, R., Glauert, J.R., Kennaway, J., Marshall, I.: The development of language processing support for the ViSiCAST project. In: Proceedings of the Fourth International ACM Conference on Assistive Technologies, pp. 101–108. ACM (2000)

    Google Scholar 

  10. Elliott, R., Glauert, J.R., Kennaway, J., Marshall, I., Safar, E.: Linguistic modelling and language-processing technologies for avatar-based sign language presentation. Univ. Access Inf. Soc. 6(4), 375–391 (2008)

    Article  Google Scholar 

  11. Forster, J., Schmidt, C., Koller, O., Bellgardt, M., Ney, H.: Extensions of the sign language recognition and translation corpus RWTH-PHOENIX-Weather (2014)

    Google Scholar 

  12. Fuchs, M., Tsourakis, N., Rayner, M.: A scalable architecture for web deployment of spoken dialogue systems. In: Proceedings of LREC 2012, Istanbul, Turkey (2012)

    Google Scholar 

  13. Huenerfauth, M.: Generating American Sign Language classifier predicates for English-to-ASL machine translation. Ph.D. thesis, University of Pennsylvania (2006)

    Google Scholar 

  14. Johnston, T., Schembri, A.: Australian Sign Language (Auslan): An Introduction to Sign Language Linguistics. Cambridge University Press, Cambridge (2007)

    Book  Google Scholar 

  15. Kacorri, H., Lu, P., Huenerfauth, M.: Effect of displaying human videos during an evaluation study of American Sign Language animation. ACM Trans. Accessible Comput. 5, 1–31 (2013)

    Article  Google Scholar 

  16. Kennaway, R.: Synthetic animation of deaf signing gestures. In: Wachsmuth, I., Sowa, T. (eds.) GW 2001. LNCS (LNAI), vol. 2298, p. 146. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  17. Marshall, I., Safar, E.: Grammar development for sign language avatar-based synthesis. In: Proceedings HCII, pp. 1–10 (2005)

    Google Scholar 

  18. Mazzei, A., Lesmo, L., Battaglino, C., Vendrame, M., Bucciarelli, M.: Deep natural language processing for Italian Sign Language translation. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds.) AI*IA 2013. LNCS, vol. 8249, pp. 193–204. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  19. Morrissey, S.: Data-driven machine translation for sign languages. Ph.D. thesis, Dublin City University, Dublin, Ireland (2008)

    Google Scholar 

  20. Morrissey, S., Way, A., Stein, D., Bungeroth, J., Ney, H.: Combining data-driven MT systems for improved sign language translation (2007)

    Google Scholar 

  21. Neidle, C.J., Kegl, J., MacLaughlin, D., Bahan, B., Lee, R.G.: The Syntax of American Sign Language: Functional Categories and Hierarchical Structure. MIT Press, Cambridge (2000)

    Google Scholar 

  22. Ong, S.C., Ranganath, S.: Automatic sign language analysis: a survey and the future beyond lexical meaning. IEEE Trans. Pattern Anal. Mach. Intell. 27(6), 873–891 (2005)

    Article  Google Scholar 

  23. Patil, S., Davies, P.: Use of Google Translate in medical communication: evaluation of accuracy. BMJ 349, g7392 (2014)

    Article  Google Scholar 

  24. Prillwitz, S., Leven, R., Zienert, H., Hanke, T., Henning, J.: HamNoSys: Version 2.0; Hamburg Notation System for Sign Languages; An Introductory Guide. Signum-Verlag (1989)

    Google Scholar 

  25. Rayner, M., Baur, C., Chua, C., Bouillon, P., Tsourakis, N.: Helping non-expert users develop online spoken CALL courses. In: Proceedings of the Sixth SLaTE Workshop, Leipzig, Germany (2015)

    Google Scholar 

  26. Salihu, M.: Piloting the first-ever RAMP survey using DHIS mobile. In: 141st APHA Annual Meeting, 2–6 November 2013. APHA (2013)

    Google Scholar 

  27. Stein, D., Schmidt, C., Ney, H.: Analysis, preparation, and optimization of statistical sign language machine translation. Mach. Transl. 26, 325–357 (2012). Accessed 18 Mar. 2012

    Article  Google Scholar 

  28. Su, H.Y., Wu, C.H.: Improving structural statistical machine translation for sign language with small corpus using thematic role templates as translation memory. IEEE Trans. Audio Speech Lang. Process. 17(7), 1305–1315 (2009)

    Article  MathSciNet  Google Scholar 

  29. Tomaszewski, P., Farris, M.: Not by the hands alone: functions of non-manual features in Polish Sign Language. In: Bokus, B. (ed.) Studies in the Psychology of Language and Communication, pp. 289–320 (2010)

    Google Scholar 

  30. Tsourakis, N., Estrella, P.: Evaluating the quality of mobile medical speech translators based on ISO/IEC 9126 series: definition, weighted quality model and metrics. Int. J. Reliable Qual. E Healthc. (IJRQEH) 2(2), 1–20 (2013)

    Article  Google Scholar 

  31. Zeshan, U.: Sprachvergleich: Vielfalt und Einheit von Gebärdensprachen. In: Eichmann, H., Hansen, M., Heßmann, J. (eds.) Handbuch Deutsche Gebärden-sprache, pp. 311–340. Signum, Hamburg (2012)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. FTI/TIM, University of Geneva, Geneva, Switzerland

    Manny Rayner, Alejandro Armando, Pierrette Bouillon, Johanna Gerlach, Sonia Halimi, Irene Strasly & Nikos Tsourakis

  2. Institute of Computational Linguistics, University of Zurich, Zurich, Switzerland

    Sarah Ebling

Authors
  1. Manny Rayner
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alejandro Armando
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierrette Bouillon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sarah Ebling
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Johanna Gerlach
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Sonia Halimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Irene Strasly
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nikos Tsourakis
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Manny Rayner .

Editor information

Editors and Affiliations

  1. University of Seville, Seville, Spain

    José F. Quesada

  2. University of Seville, Seville, Spain

    Francisco-Jesús Martín Mateos

  3. University of Seville, Seville, Spain

    Teresa Lopez-Soto

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Rayner, M. et al. (2016). Helping Domain Experts Build Phrasal Speech Translation Systems. In: Quesada, J., Martín Mateos, FJ., Lopez-Soto, T. (eds) Future and Emergent Trends in Language Technology. FETLT 2015. Lecture Notes in Computer Science(), vol 9577. Springer, Cham. https://doi.org/10.1007/978-3-319-33500-1_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-33500-1_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33499-8

  • Online ISBN: 978-3-319-33500-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation