Skip to main content

Speech coding with multilayer networks

  • Conference paper
  • 655 Accesses

Part of the book series: NATO ASI Series ((NATO ASI F,volume 68))

Abstract

A set of Multi-Layered Networks (MLN) for Automatic Speech Recognition (ASR) is proposed. Such a set allows to integrate information extracted with variable resolution in the time and in the frequency domain and to keep the number of links between nodes of the networks small in order to allow significant generalization during learning with a reasonable training set size.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Jelinek, F.: The development of an experimental discrete dictation recognizer. IEEE Proceedings, pp. 1616–1624, (November 1984).

    Google Scholar 

  2. Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. In Parallel Distributed Processing: Exploration in the Microstracture of Cognition, vol. 1, MIT Press, 318–362, (1986).

    Google Scholar 

  3. Plout, D.C., Hintön, G.E.: Learning sets of filters using back propagation. Computer Speech and Language, vol. 2, 35–61, (1987).

    Article  Google Scholar 

  4. Hinton, G.E., Sejnowski, T.J.: Learning and relearning in Boltzmann machines. In Parallel Distributed Processing: Exploration in the Microstracture of Cognition, vol. 1, MIT Press, 282–317, (1986).

    Google Scholar 

  5. Bourlard, H., Wellekens, C J.: Links between Markov models and multilayer perceptron. IEEE Conference on Neural Networks, Denver Co., (1988).

    Google Scholar 

  6. Watrous, R.L., Shastri, L.: Learning phonetic features using connectionist networks. Proceedings of the 10th International Joint Conference on Artificial Intelligence, 851–854, (1987).

    Google Scholar 

  7. Waibel, A., Hanazawa, T., Hinton, G.E., Shikano, K., Lang, K.: Phoneme recognition: neural networks vs hidden Markov models. IEEE Transactions on on Acoustics, Speech and Signal Processing, (1989).

    Google Scholar 

  8. Gori, M., Bengio Y., De Mori, R.: BPS: A learning algorithm for capturing the dynamic nature of speech. In Proceedings ICNN-89, Washington, D. C, (1989).

    Google Scholar 

  9. Bengio Y., De Mori, R.: Speaker normalization and automatic speech recognition using spectral lines and neural networks. In Proceedings of the Canadian Conference on Artificial Intelligence (CSCSI-88), Edmonton, Al., (May 1988).

    Google Scholar 

  10. Cosi, P., Bengio Y., De Mori, R.: On the generalization capabilities of multilayer networks in the extraction of speech properties. In Proceedings of the 11th International Joint Conference on Artificial Intelligence, Detroit Mi., (Aug. 1989).

    Google Scholar 

  11. De Mori, R., Lam, L., Gilloux, M.: Learning and plan refinement in a knowledge-based system for automatic speech recognition. In IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-9, No.2, 289–305, (1987).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1990 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Bengio, Y., Cardin, R., Cosi, P., De Mori, R., Merlo, E. (1990). Speech coding with multilayer networks. In: Soulié, F.F., Hérault, J. (eds) Neurocomputing. NATO ASI Series, vol 68. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76153-9_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-76153-9_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-76155-3

  • Online ISBN: 978-3-642-76153-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics