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Modelling the Emergence of Speech and Language Through Evolving Connectionist Systems

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Future Directions for Intelligent Systems and Information Sciences

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 45))

Abstract

This chapter presents the hypothesis that language knowledge evolves in the human brain through incremental learning and that the process can be modelled with the use of evolving connectionist systems — a recently introduced neural network paradigm. Several assumptions have been hypothesised and proven through simulation: (a) the learning system evolves its own representation of spoken language categories (phonemes) in an unsupervised mode through adjusting its structure to continuously flowing examples of spoken words (a learner does not know in advance which phonemes there are going to be in a language, nor, for any given word, how many phonemes segments it has); (b) learning words and phrases is associated with supervised presentation of meaning; (c) it is possible to build a ‘life-long’ learning system that acquires spoken languages in an effective way, possibly faster than humans, provided there are fast machines to implement the evolving, learning models.

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References

  1. Altman, G., Cognitive Models of Speech Processing, MIT Press, 1990.

    Google Scholar 

  2. Amari, S. and Kasabov, N. eds, “Brain-like Computing and Intelligent Information Systems”, Springer Verlag,1997.

    MATH  Google Scholar 

  3. Bates, E. and J. Goodman. On the emergence of grammar from the lexicon. In: B. MacWhinney (ed), The Emergence of Language, 29–79. Mahweh: Lawrence Erlbaum. 1999.

    Google Scholar 

  4. Brent, M. Advances in the computational stu dy of language acquisition, in: M. Brent (ed) Computational Approaches to Language Acquisition (1996)

    Google Scholar 

  5. Bybee, J. (1995). Regular morphology and the lexicon. Language and Cognitive Processes 10, 425–455.

    Article  Google Scholar 

  6. Carpenter, G. and Grossberg S., Pattern recognition by self-organizing neural networks, The MIT Press, Cambridge, Massachusetts (1991)

    Google Scholar 

  7. Chomsky, N., The Minimalist Program, Cambridge, MA: MIT Press, 1995.

    MATH  Google Scholar 

  8. Cole, R. et al. “The Challenge of Spoken Language Systems: Research Directions for the Nineties”, IEEE Transactions on Speech and Audio Processing, vol.3, No.l, 1–21, 1995

    Article  Google Scholar 

  9. Culicover, P. Syntactic Nuts: Hard cases, Syntactic theory, and Language Acquisition. Oxford: Oxford University Press. 1999.

    Google Scholar 

  10. Damasio, H., Grabowski, T., Tranel, D., Hichwa, D., Damsio, A. A neural basis for lexical retrieval, Nature, 380, April, 499–505 (1996)

    Article  Google Scholar 

  11. Deacon, T. The symbolic species (The co-evolution of language and the human brain) Penguin Press, 1998

    Google Scholar 

  12. Drossaers, M. Little Linguistic Creatures (Closed Systems of Solvable Neural Networks for Integrated Linguistic Analysis), PhD Thesis, University of Twente, Enschede (1995)

    Google Scholar 

  13. Durand, G., Kovalchuk, Y., Konnerth, A. Long-term potentiation and functional synapse induction in developing hippocampus, Nature, 381, May, 71–75 (1996)

    Article  Google Scholar 

  14. Edelman, G., Neuronal Darwinism: The theory of neuronal group selection, Basic Books (1992)

    Google Scholar 

  15. Elman, J., E. Bates, M. Johnson, A. Karmiloff-Smith, D. Parisi and K. Plunkett, Rethinking Innateness (A Connectionist Perspective of Development), The MIT Press, 1997

    Google Scholar 

  16. Fletcher and Macwhinney (1996). The Handbook of Child Language. Oxford: Blackwell

    Book  Google Scholar 

  17. Fodor, J. and Pylyshyn, Z. (1988) “Connectionism and Cognitive Architecture: a Critical Analysis,” Cognition 28, 3–71

    Article  Google Scholar 

  18. Harris, C. Connectionism and Cognitive Linguistics, Connection Science, vol.2, 1&2 (1990), 7–33

    Article  Google Scholar 

  19. Jusczyk, P. The Discovery of Spoken Language, The MIT Press, 1997

    Google Scholar 

  20. Kasabov, N. (1996) Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, MIT Press, Cambridge, Massachusetts, 550 pages

    MATH  Google Scholar 

  21. Kasabov, N. Brain-like functions in evolving connectionist systems for on-line, knowledge-based learning, in: T. Kitamura (ed) Modelling brain functions for intelligent systems, World Scientific — FLSI Series (2000)

    Google Scholar 

  22. Kasabov, N. ECOS: A framework for evolving connectionist systems and the eco learning paradigm, Proc. of ICONIF98, Kitakyushu, Oct. 1998, IOS Press, 1222–1235

    Google Scholar 

  23. Kasabov, N. Evolving fuzzy neural networks for on-line, knowledge based learning, IEEE Transactions on Systems, Man, and Cybernetics (2000)

    Google Scholar 

  24. Kasabov, N. Evolving Fuzzy Neural Networks — Algorithms, Applications and Biological Motivation, in: Yamakawa and Matsumoto (eds), Methodologies for the Conception, design and Application of Soft Computing, World Scientific, 1998, 271–274

    Google Scholar 

  25. Kasabov, N. The ECOS Framework and the ECO Learning Method for Evolving Connectionist Systems, Journal of Advanced Computational Intelligence, 2 (6) 1998, 195–202

    Google Scholar 

  26. Kasabov, N., “A framework for intelligent conscious machines utilising fuzzy neural networks and spatial temporal maps and a case study of multilingual speech recognition”, in: Journal of Advanced Computational Intelligence, 106–126 (1998)

    Google Scholar 

  27. Kasabov, N., R. Kozma, R. Kilgour, M. Laws, J. Taylor, M. Watts, and A. Gray, “A Methodology for Speech Data Analysis and a Framework for Adaptive Speech Recognition Using Fuzzy Neural Networks and Self Organising Maps”, in: Kasabov and Kozma (eds) Neuro-fuzzy techniques for intelligent information systems, Physica Verlag (Springer Verlag) 1999

    Google Scholar 

  28. Kasabov, N., R.Kilgour, and S.Sinclair, From hybrid adjustable neuro-fuzzy systems to adaptive connectionist-based systems for phoneme and word recognition, Fuzzy Sets and Systems, 103 (1999) 349–367

    Article  Google Scholar 

  29. Kim, K., Relkin, N., Min-lee, K., Hirsch, J. Distinct cortical areas associated with native and second languages, Nature, 388, July (1997)

    Google Scholar 

  30. Kohonen, T., “The Self-Organizing Map”, Proceedings of the IEEE, vol.78, N-9, pp.l464–1497,(1990).

    Article  Google Scholar 

  31. Kgpke, K.M.. (1988). Schemas in German plural formation. Lingua 74: 303–335.

    Article  Google Scholar 

  32. Kuhl, P. (1994) Speech Perception, in: Introduction to Communication Sciences and Discorse, F. Minifie (ed), san Diego

    Google Scholar 

  33. , Singular P., 77–142

    Google Scholar 

  34. Lakoff, G. and M. Johnson (1999). Philosophy in the Flesh. Basic Books, New York

    Google Scholar 

  35. Langacker, R. (1986, 1991). Foundations of Cognitive Grammar. 2 vols. Stanford University Press.

    Google Scholar 

  36. Li, Ping and Brian MacWhinney (1996). Cryptotype, over-generalisation and competition: A connectionist model of the learning of English reverse prefixes. Connection Science 8: 3–30.

    Article  Google Scholar 

  37. Liberman, A., F. Cooper, D. Shankweiler and M. Studdert-Kennedy, Perception of the speech code. Psychological Review 74,431–461. 1967.

    Article  Google Scholar 

  38. Lindblom, B. Spectrographic study of vowel reduction. Journal of the Acoustic Society of America, 35, 1773–1781. 1963

    Article  Google Scholar 

  39. Matsumotot, G., M. Ichikawa, Y. Shigematsu (1996) Brain Computing, In: Methodologies for Conception, Design, and Application of Intelligent Systems, World Scientific, 15–24

    Google Scholar 

  40. McClelland, J., B.L. McNaughton, and R.C. Reilly “Why there are Complementary Learning Systems in the Hippocampus and Neocortx: Insights from the Successes and Failures of Connectionist Models of Learning and Memeory”, CMU Technical Report PDP.CNS.94.1, March, (1994)

    Google Scholar 

  41. McClelland, J., Rumelhart, D., et. al. Parallel Distributed Processing, vol. II, MIT Press (1986)

    Google Scholar 

  42. Nijholt, A. and J. Hulstijn (eds). Formal Semantics and Pragmatics of Dialogue. Proceedings Twendial98 (TWLT13), Universty of Twente, The Netherlands, May 1998

    Google Scholar 

  43. Parisi, D. (1997). An artificial life approach to language. Brain and Language 59, 121–146

    Article  Google Scholar 

  44. Pinker, S. (1994) The Language Instinct: How the Mind Creates Language. Penguin

    Google Scholar 

  45. Plunkett, K. (1996). Connectionist approaches to language acquisition. In Fletcher & MacWhinney (eds), The Handbook of Child Language. Oxford: Blackwell, 36–72

    Google Scholar 

  46. Port, R., and T.van Gelder (eds) Mind as motion (Explorations in the Dynamics of Cognition), The MIT Press, 1995

    Google Scholar 

  47. Regier, T. (1996). The Human Semantic Potential: Spatial Language and Constrained Connectionism. MIT Press.

    Google Scholar 

  48. Rummelhart, D. and McClelland, J. (1985) “On Learning the Past Tenses of English Verbs,” in McClelland and Rummelhart et. al. (1986) Ch. 18, pp. 216–271

    Google Scholar 

  49. Segalowitz, S.J. Language functions and brain organisation, Academic Press, 1983

    Google Scholar 

  50. Seidenberg, M. Language acquisition and use: learning and applying probabilistic constraints, Science, 275, 1599–603 (1997)

    Article  Google Scholar 

  51. Shastri, L. A biological grounding of recruitment learning and vicinal algorithms, TR-99–009, International Computer Science Institute, Berkeley, (1999)

    Google Scholar 

  52. Shastri, L. and C. Wendelken, Knowledge fusion in the large — taking a cue from the brain, Int. Conf. FUSION’99, Sunnyvale, June 1999

    Google Scholar 

  53. Snow, C. and C. Ferguson. Talking to children: Language input and language acquisition. Cambridge: Cambridge University Press. 1977.

    Google Scholar 

  54. Stephens, C, Olmedo, I., Vargas, J., Waelbroack, H., Self adaptation in evolving systems, Artificial Life, 4(2) 183–201

    Article  Google Scholar 

  55. Stork, D. (1991) Sources of neural structure in speech and language processing, Int. Journ. on Neural Systems, Singapore, 2 (3), 159–167

    Article  Google Scholar 

  56. Taylor, J. R. & MacLaury, R. E. (eds.), Language and the Cognitive Construal of the World. Berlin, Mouton de Gruyter (1995) 406p.

    Google Scholar 

  57. Taylor, J. R. An Introduction to Cognitive Linguistics. Oxford, Clarendon Press, 1999, to appear

    Google Scholar 

  58. Taylor, J. R. Linguistic Categorization: Prototypes in Linguistic Theory. 2nd Edition. Oxford, Clarendon Press (1995)

    Google Scholar 

  59. Taylor, J., N. Kasabov and R. Kilgour, Modelling the emergence of speech sound categories in evolving connectionist systems, in: Proc. Joint. Conf. Information Sciences — JCIS, Atlantic City, 2000

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Linguistics, University of Otago, P.O Box 56, Dunedin, New Zealand

    John Taylor

  2. Department of Information Science, University of Otago, P.O Box 56, Dunedin, New Zealand

    Nikola Kasabov

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  1. John Taylor
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  2. Nikola Kasabov
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Editors and Affiliations

  1. Department of Information Science, University of Otago, P.O. Box 56, Dunedin, New Zealand

    Nikola Kasabov

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Taylor, J., Kasabov, N. (2000). Modelling the Emergence of Speech and Language Through Evolving Connectionist Systems. In: Kasabov, N. (eds) Future Directions for Intelligent Systems and Information Sciences. Studies in Fuzziness and Soft Computing, vol 45. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1856-7_6

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  • DOI: https://doi.org/10.1007/978-3-7908-1856-7_6

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-7908-2470-4

  • Online ISBN: 978-3-7908-1856-7

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