Skip to main content

Language Models Comparison in a Robot Telecontrol Application

  • Conference paper
Speech Recognition and Coding

Part of the book series: NATO ASI Series ((NATO ASI F,volume 147))

  • 226 Accesses

Abstract

Stochastic Language Models (LMs) are key for achieving good performance in speech recognition systems. This is confirmed by the numerous LMs that have been proposed recently in the literature. This work compares three different LMs within the robot telecontrol speech understanding system developed at IRST.

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 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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.

References

  1. G. Antoniol, M. Cettolo, and M. Federico. Techniques for robust recognition in restricted domains. In Proceedings of the 3rd European Conference on Speech Communication and Technology, Berlin, Germany, 1993

    Google Scholar 

  2. L. E. Baum and J. A. Egon. An inequality with applications to statistical prediction for functions of Markov processes and to a model for ecology. Bull. Amer. Math. Soc, 73: 360–363, 1967

    Article  MathSciNet  MATH  Google Scholar 

  3. L. Breiman, J. H. Friedman, R. O. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth, Pacific Grove, CA, 1984

    Google Scholar 

  4. A. Corazza, M. Federico, R. Gretter, and G. Lazzari. Design and acquisition of a task-oriented spontaneous-speech data base. In V. Roberto (ed.), Research Topics on Intelligent Perceptual Systems, Springer-Verlag, Berlin, Germany, 1993

    Google Scholar 

  5. A.-M. Derouault and B. Merialdo. Natural language modeling for phoneme-to-text transcription. IEEE Trans. Pattern Anal. Machine Intell., PAMI-8(6): 742–749, 1986

    Article  Google Scholar 

  6. F. Jelinek, R. L. Mercer, and S. Roukos. Principles of lexical language modeling for speech recognition. In S. Furui and M. M. Sondhi (eds.), Advances in Speech Signal Processing, pp. 651–699. Marcel Dekker, Inc., New York, NY, 1992

    Google Scholar 

  7. P. Placeway, R. Schwartz, P. Fung, and L. Nguyen. The estimation of powerful language models from small and large corpora. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, Vol. II, pp. 33–36, Minneapolis, MN, 1993

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1995 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Antoniol, G., Brugnara, F., Cettolo, M., Federico, M. (1995). Language Models Comparison in a Robot Telecontrol Application. In: Ayuso, A.J.R., Soler, J.M.L. (eds) Speech Recognition and Coding. NATO ASI Series, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57745-1_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-57745-1_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-63344-7

  • Online ISBN: 978-3-642-57745-1

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics