Knowledge-Based Computer Recognition of Speech

  • Renato De Mori
Conference paper
Part of the NATO ASI Series book series (volume 30)


At present, a number of scientists and engineers seem to be quite interested in doing research in the area of speech recognition by computer. Different workers in the field have different approaches, and might even describe their motivations for doing speech recognition research somewhat differently. A very common position, for example, is that the main goal of speech recognition research is to develop techniques and systems for speech input to machines. If we consider machines which are real computers, rather than mere automatic dictation devices, this makes speech recognition an instance of the general problem of designing a convenient and pleasant human-computer interface, the ultimate goal being the ability to talk to computers in much the same way we now talk to fellow human beings. Indeed, if both speech recognition and general machine intelligence make sufficient progress in our lifetimes, we could conceivably encounter computers that not only listen but also reply sensibly.


Speech Recognition Acoustic Property Speech Recognition System Phonetic Feature Continuous Speech Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Bahl, L.R., Jelinek, F., Mercer, R.L., “A Maximum Likelihood Approach to Continuous Speech Recognition,” IEEE Trans, on Pattern Analysis and Machine Intelligence, Vol. PAMI-5, No. 2, pp. 179–190, March 1983.Google Scholar
  2. [2]
    Bahl, L.R., Das, S.K., de Souza, P.V., Jelinek, F., Katz, S., Mercer, R.L., Picheny, M.A., “Some Experiments with Large-Vocabulary Isolated Word Sentence Recognition,” Proc. of the IEEE Conference on Acoustics, Speech, and Signal Processing, San Diego, CA, pp. 2651–2653, March 1984.Google Scholar
  3. [3]
    Church, K.W., “Phrase-Structure Parsing: A Method for Taking Advantage of Allophonic Constraints,” MIT/LCS/TR-296, Cambridge, MA, January 13, 1983. (MIT Ph.D. thesis)Google Scholar
  4. [4]
    Demichelis, P., De Mori, R., Laface, P. and O’Kane, M., “Computer Recognition of Plosive Sounds Using Contextual Information,” IEEE Trans, on Acoustics, Speech, and Signal Processing, Vol. ASSP-31, No. 2, pp. 359–377, April 1983.Google Scholar
  5. [5]
    De Mori, R., Giordana, A., Laface, P., Saitta, L., “An Expert System for Interpreting Speech Patterns,” Proc. of the AAAI-82, pp. 107–110, 1982.Google Scholar
  6. [6]
    De Mori, R., Computer Models of Speech Using Fuzzy Algorithms, Plenum Press, New York, NY, 1983.CrossRefGoogle Scholar
  7. [7]
    De Mori, R. and Gilloux, M., “Inductive Learning of Phonetic Rules for Automatic Speech Recognition,” Proc. of the CSCSI-84, London, Ontario, pp. 103–106, May 1984.Google Scholar
  8. [8]
    De Mori, R., Laface, P., and Mong, Y., “Parallel Algorithms for Syllable Recognition in Continuous Speech,” IEEE Trans, on Pattern Analysis and Machine Intelligence, Vol. PAMI-6, pp. 56–69, January 1985.Google Scholar
  9. [9]
    Doyle, J., “A Truth Maintenance System,” Artificial Intelligence, Vol. 12, No. 3, pp. 231–272, 1979.MathSciNetCrossRefGoogle Scholar
  10. [10]
    Erman, L.D., Hayes-Roth, F., Lesser, V.R., Reddy, D.R., “The HEARSAY-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty,” Computing Surveys, Vol. 12, No. 2, pp. 213–253, June 1980.CrossRefGoogle Scholar
  11. [11]
    Kopec, G.E., “Voiceless Stop Consonant Identification Using LPC Spectra,” Proc. of the IEEE Conference on Acoustics, Speech, and Signal Processing, San Diego, CA, pp. 4211–4214, March 1984.Google Scholar
  12. [12]
    Levinson, S., Rabiner, L.R., “Isolated and Connected Word Recognition: Theory and Selected Applications,” IEEE Trans, on Communications, Vol. COM-29, No. 5, pp. 621–659, May 1981.Google Scholar
  13. [13]
    McCarthy, J., “Some Expert Systems Need Common Sense,” in The Computer Culture, H. Pageis, ed., Annals of the New York Academy of Sciences, Vol. 426, (1984).Google Scholar
  14. [14]
    Michalski, R.S., “A Theory and Methodology of Inductive Learning,” in Machine Learning: An Artificial Intelligence Approach, Tioga Publishing Company, Palo Alto, CA, pp. 83–134, 1983.Google Scholar
  15. [15]
    Minsky, M., “A Framework for Representing Knowledge,” in The Psychology of Computer Vision, P. Winston, ed., McGraw-Hill, New York, NY, 1975.Google Scholar
  16. [16]
    Moses, J., “Computer Science as the Science of Discrete Man-Made Systems,” Knowledge: Creation, Diffusion, Utilization, Vol. 4, No. 2, pp. 219–226, December 1982, reprinted in The Study of Information: Interdisciplinary Messages, F. Machlup and U. Mansfield, eds., John Wiley and Sons, New York, NY, 1983.Google Scholar
  17. [17]
    Neisser, U., Cognition and Reality: Principles and Implications of Cognitive Psychology, W.H. Freeman and Co., San Francisco, CA, 1976.Google Scholar
  18. [18]
    Rabiner, L.R., Wilpon, J.G., Terrace, S.G., “A Directory Listing Retrieval System Based on Connected Letter Recognition,” Proc. IEEE Conference on Acoustics, Speech, and Signal Processing, San Diego, CA, pp. 3541–3544, March 1984.Google Scholar
  19. [19]
    Whitehill, S.B., “Self Correcting Generalization,” Proc. of the AAAI-80, pp. 240–242, 1980.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1987

Authors and Affiliations

  • Renato De Mori
    • 1
  1. 1.School of Computer ScienceMcGill UniversityMontréalCanada

Personalised recommendations