Connectionist Inference Models

The performance of symbolic inference tasks has long been a challenge to connectionists. In this paper, we present an extended survey of this area. Existing connectionist inference systems are reviewed, with particular reference to how they perform variable binding and rule-based reasoning, and whether they involve distributed or localist representations. The benefits and disadvantages of different representations and systems are outlined, and conclusions drawn regarding the capabilities of connectionist inference systems when compared with symbolic inference systems or when used for cognitive modeling

Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid system

Abstract The paper considers both knowledge acquisition and knowledge interpretation tasks as tightly connected and continuously interacting processes in a contemporary knowledge engineering system. Fuzzy rules are used here as a framework for knowledge representation. An algorithm REFuNN for fuzzy rules extraction from adaptive fuzzy neural networks (FuNN) is proposed. A case study of Iris classification is chosen to illustrate the algorithm. Interpretation of fuzzy rules is possible by using fuzzy neural networks or by using standard fuzzy inference methods. Both approaches are compared in the paper based on the case example. A hybrid environment FuzzyCOPE which facilitates neural network simulation, fuzzy rules extraction from fuzzy neural networks and fuzzy rules interpretation by using different methods for approximate reasoning is briefly described

Connectionist-based information systems: a proposed research theme

This PDF was created from a converted WordPerfect document. While all reasonable efforts have been made to reproduce the original paper as closely as possible, some formatting in the PDF may vary from the original hard-copy paper.General Characteristics of the Theme • Emerging technology with rapidly growing practical applications • Nationally and internationally recognised leadership of the University of Otago • Already established organisation for research and working teams • Growing number of postgraduate students working on the theme • Growing number of research projects in this area • Growing number of publications by members of the teamUnpublishedThe following is a selected list of some of the major publications published by the principal researchers in the area of CBIS in the last several years. (a) Books Kasabov N.K. Neural Networks, Fuzzy Systems and Knowledge Engineering. Cambridge, MA, MIT Press (being printed) 550p Sallis P.J., Tate G. and MacDonell S.G. Software Engineering: practice, management and improvement. Addison-Wesley, 1995, 215p (b) Book Chapters Kasabov N. and Clarke G. A template-based implementation of connectionist knowledge based systems for classification and learning. In Advances in Neural Networks, vol.4. O.Omidvar ed. USA, Ablex Publishing Company (1995) 137-156 Kasabov N. Building comprehensive AI and the task of speech recognition. In Applications of Neural Networks to Telecommunications, 2, ed. J.Alspector, R.Goodman, T.Brown, Laurence Erlbaum (1995) 178-187 Kasabov, N. and Nikovski, D. Prognostic expert systems on a hybrid connectionist environment. In Artificial Intelligence V Methodology, Systems, Applications. B. du Boulay and V.Sgurev eds., Elsevier Science Publ, North Holland (1992) 141-148 Kasabov N. Hybrid connectionist rule based systems. In Artificial Intelligence IV Methodology, Systems, Applications. P.Jorrand and V.Sgurev Eds., North-Holland (1990) 227- 235 (c) Publications in Refereed Journals Kasabov, N., Lavington S., Li S. and Wang C. A model for exploiting parallel associative matching in AI production systems. Knowledge-Based Systems 8(1):1-7 (1995) Kasabov, N. Adaptable connectionist production systems. Neurocomputing (1995) (being printed) Kasabov, N. Hybrid connectionist fuzzy systems for speech recognition. Lecture Notes in Computer Science/Artificial Intelligence (1995) (being printed) Kasabov, N. Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets and Systems (1995) (being printed) Kasabov, N. Hybrid Connectionist Fuzzy Production Systems - Towards Building Comprehensive AI, Intelligent Automation and Soft Computing, (1995) (being printed) Kasabov, N. Connectionist fuzzy production systems. in: Fuzzy Logic in Artificial Intelligence, A. Ralescu ed, Lecture Notes in Artificial Intelligence 847:114-128 (1994) Kasabov, N. Hybrid connectionist production systems. Journal of Systems Engineering 3 (1), 15- 21 (1993) Kasabov, N. and Shishkov, S. A connectionist production system with partial match and its use for approximate reasoning. Connection Science 5(3&4):275-305 (1993) Kasabov, N. Incorporating neural networks into production systems and a practical approach towards the realization of fuzzy expert systems. Computer Science and Informatics 21(2):26-34 (1991) M. K. Purvis, M. A. Purvis, and G. L. Benwell, "Modelling and Simulation of the New Zealand Resource Management Act", to appear in the Journal of Law and Information Science, 1995. S. Cranefield, P. Gorman, and M. Purvis, "Communicating Agents: An Emerging Approach for Distributed Heterogeneous Systems", New Zealand Journal of Computing, 6:1B, August 1995, pp. 337-343. M. K. Purvis, G. L. Benwell, and M. A. Purvis, "Dynamic Modelling of the Resource Management Act", New Zealand Journal of Computing, 5:1, 1994, pp. 45-56. M. K. Purvis, G. L. Benwell, and M. A. Purvis, "Dynamic Modelling of the Resource Consent Process in the Resource Management Act", New Zealand Surveyor (Journal of the New Zealand Institute of Surveyors), No. 285, March 1995, pp. 13-20. Sallis, P.J., Anderson, R. and Yeap, W.K. Enhancing a hypertext application using Natural Language Processing techniques. Journal of Information Science, 17(1), 49-56 (1991) Benwell,G.L., Firns, P.G. and Sallis, P.J. Deriving semantic data models from structured descriptions of reality. Journal of Information Technology, 6(1), 15-25 (1991) Sallis, P.J. Contemporary Computing Methods for solving the authorship characterisation problem in computational linguistics. New Zealand Journal of Computing, 5(1), 85-91 (1994) (d) Publications in Proceedings of International Conferences N.Kasabov (ed) Proceedings of the First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, IEEE Computer Society Press, Los Alamitos, California, 1993. N.Kasabov and G.Coghill (eds) Proceedings of the Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, IEEE Computer Society Press, Los Alamitos, California, 1995. M. K. Purvis (ed.), Software Education Conference (SRIG-ET'94), IEEE Computer Society Press, Los Alamitos, CA, 1995. M. K. Purvis and X. Li, "Connectionist Learning Using an Optical Thin-Film Model", to appear in Proceedings of the Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, IEEE Computer Society Press, Los Alamitos, California, 1995. M. K. Purvis, and S. J. S. Cranefield, "Causal Agent Modelling: a Unifying Paradigm for Systems and Organisations", Proceedings of Pan-Pacific Conference XII, Dunedin and Queenstown, New Zealand, 1995, pp. 394-396. M. K. Purvis and M. A. Purvis, "Modelling Environmental Legislative Processes with Petri Nets", Modelling and Simulation (Proceedings of the International Association of Science and Technology for Development International Conference), IASTED-ACTA Press, Anaheim, CA, 1995, pp. 238-246. M. K. Purvis and L. Xiaodong, "Connectionist Computations Based on an Optical Thin- Film Model", in Proceedings of the First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, IEEE Computer Society Press, Los Alamitos, California, 1993, pp. 130-133. M. K. Purvis and G. L. Benwell, "A Causal Agent Approach for Modelling Dynamic Systems", in Proceedings of the 13th New Zealand Computer Society Conference, New Zealand Computer Society, Auckland, August 1993, pp. 598-604. D. W. Franke and M. K. Purvis, "Hardware/Software CoDesign: A Perspective", in Proceedings of the 13th International Conference on Software Engineering, Austin, TX, U.S.A., May 13-16, 1991, pp. 344-352. Sallis, P.J. Neural Networks and Ambiguity in Natural Language. Proceedings of the First New Zealand International Conference on Artificial Neural Networks and Expert Systems. Dunedin 1993, 102-104 Sallis, P.J. Spatially related knowledge: the multimedia experience. Proceedings of the Third Annual Spatial Information Processing Colloquium. Dunedin, May 1991, 107-118. Sallis, P.J. and Yeap, W.K. Alpha and Omega: an AI perspective. Proceedings of the XIth Annual South-East Asian Computer Confederation Conference. Kuala Lumpur, Aug 1992, Vol 2, 381-388 Sallis, P.J. Information retrieval using a natural language interface to a GIS database. Proceedings of the Second Annual Spatial Information Processing Colloquium. Dunedin, Nov 1990, 100-111. Barrow, F. and Sallis, P.J. NLP and GIS Techniques for a Railway Safety Audit Reporting System. Proceedings of the Seventh Annual Colloquium of the Spatial Information Research Centre, Eds. P.G. Firns and N.C. Sutherland, Palmerston North, 1995, 229-236

Connectionist-based information systems: a proposed research theme

This PDF was created from a converted WordPerfect document. While all reasonable efforts have been made to reproduce the original paper as closely as possible, some formatting in the PDF may vary from the original hard-copy paper.General Characteristics of the Theme • Emerging technology with rapidly growing practical applications • Nationally and internationally recognised leadership of the University of Otago • Already established organisation for research and working teams • Growing number of postgraduate students working on the theme • Growing number of research projects in this area • Growing number of publications by members of the teamUnpublishedThe following is a selected list of some of the major publications published by the principal researchers in the area of CBIS in the last several years. (a) Books Kasabov N.K. Neural Networks, Fuzzy Systems and Knowledge Engineering. Cambridge, MA, MIT Press (being printed) 550p Sallis P.J., Tate G. and MacDonell S.G. Software Engineering: practice, management and improvement. Addison-Wesley, 1995, 215p (b) Book Chapters Kasabov N. and Clarke G. A template-based implementation of connectionist knowledge based systems for classification and learning. In Advances in Neural Networks, vol.4. O.Omidvar ed. USA, Ablex Publishing Company (1995) 137-156 Kasabov N. Building comprehensive AI and the task of speech recognition. In Applications of Neural Networks to Telecommunications, 2, ed. J.Alspector, R.Goodman, T.Brown, Laurence Erlbaum (1995) 178-187 Kasabov, N. and Nikovski, D. Prognostic expert systems on a hybrid connectionist environment. In Artificial Intelligence V Methodology, Systems, Applications. B. du Boulay and V.Sgurev eds., Elsevier Science Publ, North Holland (1992) 141-148 Kasabov N. Hybrid connectionist rule based systems. In Artificial Intelligence IV Methodology, Systems, Applications. P.Jorrand and V.Sgurev Eds., North-Holland (1990) 227- 235 (c) Publications in Refereed Journals Kasabov, N., Lavington S., Li S. and Wang C. A model for exploiting parallel associative matching in AI production systems. Knowledge-Based Systems 8(1):1-7 (1995) Kasabov, N. Adaptable connectionist production systems. Neurocomputing (1995) (being printed) Kasabov, N. Hybrid connectionist fuzzy systems for speech recognition. Lecture Notes in Computer Science/Artificial Intelligence (1995) (being printed) Kasabov, N. Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems. Fuzzy Sets and Systems (1995) (being printed) Kasabov, N. Hybrid Connectionist Fuzzy Production Systems - Towards Building Comprehensive AI, Intelligent Automation and Soft Computing, (1995) (being printed) Kasabov, N. Connectionist fuzzy production systems. in: Fuzzy Logic in Artificial Intelligence, A. Ralescu ed, Lecture Notes in Artificial Intelligence 847:114-128 (1994) Kasabov, N. Hybrid connectionist production systems. Journal of Systems Engineering 3 (1), 15- 21 (1993) Kasabov, N. and Shishkov, S. A connectionist production system with partial match and its use for approximate reasoning. Connection Science 5(3&4):275-305 (1993) Kasabov, N. Incorporating neural networks into production systems and a practical approach towards the realization of fuzzy expert systems. Computer Science and Informatics 21(2):26-34 (1991) M. K. Purvis, M. A. Purvis, and G. L. Benwell, "Modelling and Simulation of the New Zealand Resource Management Act", to appear in the Journal of Law and Information Science, 1995. S. Cranefield, P. Gorman, and M. Purvis, "Communicating Agents: An Emerging Approach for Distributed Heterogeneous Systems", New Zealand Journal of Computing, 6:1B, August 1995, pp. 337-343. M. K. Purvis, G. L. Benwell, and M. A. Purvis, "Dynamic Modelling of the Resource Management Act", New Zealand Journal of Computing, 5:1, 1994, pp. 45-56. M. K. Purvis, G. L. Benwell, and M. A. Purvis, "Dynamic Modelling of the Resource Consent Process in the Resource Management Act", New Zealand Surveyor (Journal of the New Zealand Institute of Surveyors), No. 285, March 1995, pp. 13-20. Sallis, P.J., Anderson, R. and Yeap, W.K. Enhancing a hypertext application using Natural Language Processing techniques. Journal of Information Science, 17(1), 49-56 (1991) Benwell,G.L., Firns, P.G. and Sallis, P.J. Deriving semantic data models from structured descriptions of reality. Journal of Information Technology, 6(1), 15-25 (1991) Sallis, P.J. Contemporary Computing Methods for solving the authorship characterisation problem in computational linguistics. New Zealand Journal of Computing, 5(1), 85-91 (1994) (d) Publications in Proceedings of International Conferences N.Kasabov (ed) Proceedings of the First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, IEEE Computer Society Press, Los Alamitos, California, 1993. N.Kasabov and G.Coghill (eds) Proceedings of the Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, IEEE Computer Society Press, Los Alamitos, California, 1995. M. K. Purvis (ed.), Software Education Conference (SRIG-ET'94), IEEE Computer Society Press, Los Alamitos, CA, 1995. M. K. Purvis and X. Li, "Connectionist Learning Using an Optical Thin-Film Model", to appear in Proceedings of the Second New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, IEEE Computer Society Press, Los Alamitos, California, 1995. M. K. Purvis, and S. J. S. Cranefield, "Causal Agent Modelling: a Unifying Paradigm for Systems and Organisations", Proceedings of Pan-Pacific Conference XII, Dunedin and Queenstown, New Zealand, 1995, pp. 394-396. M. K. Purvis and M. A. Purvis, "Modelling Environmental Legislative Processes with Petri Nets", Modelling and Simulation (Proceedings of the International Association of Science and Technology for Development International Conference), IASTED-ACTA Press, Anaheim, CA, 1995, pp. 238-246. M. K. Purvis and L. Xiaodong, "Connectionist Computations Based on an Optical Thin- Film Model", in Proceedings of the First New Zealand International Two-Stream Conference on Artificial Neural Networks and Expert Systems, IEEE Computer Society Press, Los Alamitos, California, 1993, pp. 130-133. M. K. Purvis and G. L. Benwell, "A Causal Agent Approach for Modelling Dynamic Systems", in Proceedings of the 13th New Zealand Computer Society Conference, New Zealand Computer Society, Auckland, August 1993, pp. 598-604. D. W. Franke and M. K. Purvis, "Hardware/Software CoDesign: A Perspective", in Proceedings of the 13th International Conference on Software Engineering, Austin, TX, U.S.A., May 13-16, 1991, pp. 344-352. Sallis, P.J. Neural Networks and Ambiguity in Natural Language. Proceedings of the First New Zealand International Conference on Artificial Neural Networks and Expert Systems. Dunedin 1993, 102-104 Sallis, P.J. Spatially related knowledge: the multimedia experience. Proceedings of the Third Annual Spatial Information Processing Colloquium. Dunedin, May 1991, 107-118. Sallis, P.J. and Yeap, W.K. Alpha and Omega: an AI perspective. Proceedings of the XIth Annual South-East Asian Computer Confederation Conference. Kuala Lumpur, Aug 1992, Vol 2, 381-388 Sallis, P.J. Information retrieval using a natural language interface to a GIS database. Proceedings of the Second Annual Spatial Information Processing Colloquium. Dunedin, Nov 1990, 100-111. Barrow, F. and Sallis, P.J. NLP and GIS Techniques for a Railway Safety Audit Reporting System. Proceedings of the Seventh Annual Colloquium of the Spatial Information Research Centre, Eds. P.G. Firns and N.C. Sutherland, Palmerston North, 1995, 229-236

FuNN/2—a fuzzy neural network architecture for adaptive learning and knowledge acquisition

Please note that this is a searchable PDF derived via optical character recognition (OCR) from the original source document. As the OCR process is never 100% perfect, there may be some discrepancies between the document image and the underlying text.Fuzzy neural networks have several features that make them well suited to a wide range of knowledge engineering applications. These strengths include fast and accurate learning, good generalisation capabilities, excellent explanation facilities in the form of semantically meaningful fuzzy rules, and the ability to accommodate both data and existing expert knowledge about the problem under consideration. This paper investigates adaptive learning, rule extraction and insertion, and neural/fuzzy reasoning for a particular model of a fuzzy neural network called FuNN. As well as providing for representing a fuzzy system with an adaptable neural architecture, FuNN also incorporates a genetic algorithm in one of its adaptation modes. A version of FuNN—FuNN/2, which employs triangular membership functions and correspondingly modified learning and adaptation algorithms, is also presented in the paper.Unpublished[1] Yamakawa, T., Kusanagi, H., Uchino, E. and Miki, T., “A new Effective Algorithm for Neo Fuzzy Neuron Model”, in: Proceedings of Fifth IFSA World Congress, (1993) 1017-1020. [2] Hashiyama, T., Furuhashi, T., Uchikawa, Y., “A Decision Making Model Using a Fuzzy Neural Network”, in: Proceedings of the 2nd International Conference on Fuzzy Logic & Neural Networks, Iizuka, Japan, (1992) 1057-1060. [3] Kasabov, N., “Learning fuzzy rules and approximate reasoning in fuzzy neural networks and hybrid systems”, Fuzzy Sets and Systems, 82 (2), 1996, 135-149 [4] Kasabov, N., Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, The MIT Press, CA, MA, 1996 [5] Kasabov, N., “Adaptable connectionist production systems”. Neurocomputing, 13 (2-4), 1996, 95-117 [6] Hauptmann, W., Heesche, K., A Neural Net Topology for Bidirectional Fuzzy-Neuro Transformation, in: Proceedings of the FUZZ-IEEE/IFES, Yokohama, Japan, (1995) 1511-1518. [7] Jang, R., ANFIS: adaptive network-based fuzzy inference system, IEEE Trans. on Syst.,Man, Cybernetics, 23(3), May-June 1993, 665-685 [8] Lin, C-T., Lin, C-J., Lee, C.T., Fuzzy adaptive learning control network with on-line learning, Fuzzy Sets and Systems, 71(1), 1995, 25-45 [9] Goldberg, D., Genetic Algorithms is Search, Optimization and Machine Learning, Addison Wesley, 1989 [10] Mang, G. Lan, H., Zhang, L. “A Genetic-based method of Generating Fuzzy Rules and Membership Functions by Learning from Examples”, in: Proceedings of International Conference on Neural Information Processing (ICONIP ’95) Volume One, 1995, 335-338 [11] Kasabov, N. Hybrid Connectionist Fuzzy Production Systems - Towards Building Comprehensive AI, Intelligent Automation and Soft Computing, 1:4 (1995) 351-360) [12] Carpenter, G., “Learning, Recognition, and Prediction by ART and ARTMAP Neural Networks”, in: Proceedings of ICNN’96, IEEE Press, Volume “Plenary Panel and Special Sessions, 1996, 244-249 [13] Kasabov, N., Investigating the adaptation and forgetting in fuzzy neural networks by using the method of training and zeroing”, in: Proceedings of the International Conference on Neural Networks ICNN’96, Plenary, Panel and and Special Sessions volume,1996, 118-123 [14] Kasabov, N., Advanced Neuro-Fuzzy Engineering for Building Intelligent Adaptive Information Systems, in: L.Reznick, V.Dimitrov, J.Kacprzyk (eds.) Fuzzy Systems Design: Social and Engineering Applications, Physica-Verlag (Springer Verlag), to appear in 199

From hybrid adjustable neuro-fuzzy systems to adaptive connectionist-based systems for phoneme and word recognition

This paper discusses the problem of adaptation in automatic speech recognition systems (ASRS) and suggests several strategies for adaptation in a modular architecture for speech recognition. The architecture allows for adaptation at different levels of the recognition process, where modules can be adapted individually based on their performance and the performance of the whole system. Two realisations of this architecture are presented along with experimental results from small-scale experiments. The first realisation is a hybrid system for speaker-independent phoneme-based spoken word recognition, consisting of neural networks for recognising English phonemes and fuzzy systems for modelling acoustic and linguistic knowledge. This system is adjustable by additional training of individual neural network modules and tuning the fuzzy systems. The increased accuracy of the recognition through appropriate adjustment is also discussed. The second realisation of the architecture is a connectionist system that uses fuzzy neural networks FuNNs to accommodate both a prior linguistic knowledge and data from a speech corpus. A method for on-line adaptation of FuNNs is also presented.Unpublished[1] S. Amari, N.K. Kasabov (Eds.), Brain-like Computing and Intelligent Information Systems, Springer, Berlin, 1997. [2] Clark, C. Yallop, An Introduction to Phonetics and Phonology, Blackwell, Cambridge MA, 1990. [3] Cole et al., The challenge of spoken language systems: research directions for the Nineties, IEEE Trans. Speech Audio Process. 3 (1) (1995) 1-21. [4] Li-Min Fu, Building expert systems on neural architectures, Proc. Ist IEEE Internat. Conf. on Artificial Neural Networks, 1989, pp. 221-225. [5] Goldberg, Genetic Algorithms in Search, Optimisation and Machine Learning, Addison-Wesley, New York, 1989. [6] Q. Huo, C.-H. Lee, A study of on-line Quasi-Bayes adaptation for CDHMM-based speech recognition, Proc. IEEE Internat. Conf. on Acoustic, Speech, and Signal Processing, 1996, pp. 705-708. [7] J.S.R. Jang, ANFIS: adaptive-network-based fuzzy inference system, IEEETrans. Systems Man Cybernet. 23 (3) (1993) 665-684. [8] N.K. Kasabov, Building comprehensive AI and the task of speech recognition, in: J. Alspector, R. Goodman, T. Brown (Eds.), Applications of Neural Networks to Telecommunications 2, Lawrence Erlbaum, Hillsdale, NJ, 1995, pp. 178-185. [9] N.K. Kasabov, Hybrid connectionist fuzzy production systems - towards building comprehensive AI, Intell. Automat. Soft Comput. 1 (1995) 351-360. [10] N.K. Kasabov, Hybrid Connectionist Fuzzy Rule-based Systems for Speech Recognition, Lecture Notes in Computer Science/Artificial Intelligence, vol. 1011, Springer, Berlin, 1995, pp. 20-33. [11] N.K. Kasabov, Adaptable counectionist production systems, Neurocomputing 13 (1996) 95-117. [12] N.K. Kasabov, Foundations of Neural Networks, Fuzzy Systems and Knowledge Engineering, MIT Press, Cambridge, MA, 1996. [13] N.K. Kasabov, Learning and approximate reasoning in fuzzy neural networks and hybrid systems, Fuzzy Sets and Systems 82 (1996) 135-149. [14] N.K. Kasabov, Learning strategies for modular neuro-fuzzy systems: a case study on phoneme-based speech recognition, J. Intell. Fuzzy Systems 5 (1997) 1-10. [15] N.K. Kasabov, A framework for intelligent conscious machines and applications for adaptive speech recognition, in: Amari, N.K. Kasabov (Eds.), Brain-like Computing and Intelligent Systems, Springer, Berlin, 1997. [16] N.K. Kasabov, ECOS: Evolving connectionist systems - methods, algorithms, applications, in: Proc. ICONIP'98 Conf. (International Conference on Neuro-Information Processing), Kitakyushu, Japan, 21-23 October 1998, pp. 793-796. [17] N.K. Kasabov, J.S. Kim, M. Watts, A. Gray, FuNN/2 - a fuzzy neural network architecture for adaptive learning and knowledge acquisition, Inform. Sci. 101 (3-4) (1997) 155-175. [18] N.K. Kasabov, R. Kozma, M. Watts, Optimization and adaption of fuzzy neural networks through genetic algorithms and learning-with-forgetting methods and applications for phoneme based speech recognition, Inform. Sci. 110 (1998) 61-79. [19] N.K. Kasabov, E. Postma, J. van en Herik, AVIS: a connectionist framework for integrated audio and visual information processing systems, in: Proc. Iizuka'98 Conf., 16-20 October, Iizuka, Japan, 1998. [20] N.K. Kasabov, S.J. Sinclair, R. Kilgour, C. Watson, M. Laws, D. Kassabova, Intelligent human computer interfaces and the case study of building English-to-Māori talking dictionary, in: N.K. Kasabov, G. Coghill (Eds.), Proc. ANNES'95, Dunedin, IEEE Computer Society Press, Los Alamitos, 1995, pp. 294-297. [21] R.I, Kilgour, Hybrid fuzzy systems and neural networks for speech recognition, Unpublished Masters Thesis, University of Otago, 1996. [22] K. Kim, N. Relkin, K.-M. Lee, J. Hirsch, Distinct cortical areas associated with native and second languages, Nature 388 (1997) 171-174. [23] D. Massaro, Perceiving Talking Faces, MIT Press, Cambridge, MA, 1997. [24] D. Massaro, M. Cohen, Integration of visual and auditory information in speech perception, J. Experimental Psychol.: Human Perception Performance 9 (1983) 753-771. [25] Mitra, S. Pal, Fuzzy multi-layer perceptron, inferencing and rule generation, IEEE Trans. Neural Networks 6 (1995) 51-63. [26] Morgan, C. Scofield, Neural Networks and Speech Processing, Kluwer Academic Publishers, Amsterdam, 1991. [27] N. Pal, E. Kumar, Neural networks for dimensionality reduction, in: Kasabov et al. (Eds.), Connectionist Based Information Systems, Proc. ICONIP'97 Conf., Dunedin, Springer, Singapore, 1997, pp. 221-224. [28] R. Rabiner, Applications of voice processing to telecommunications, Proc. IEEE 82 (1994) 199-228. [29] D. Robinson, Artificial Intelligence and Expert Systems, McGraw Hill, New York, 1988. [30] G. Rummery, M. Niranjan, On-line Q-learning using connectionist systems, CUED/F-INFENG/TR 166, Cambridge University Engineering Department, 1994. [31] A. Sankar, L. Neumeyer, M. Weintraub, An experimental study of acoustic adaptation algorithms, Proc. IEEE Internat. Conf. on Acoustic, Speech, and Signal Processing, 1996, pp. 713-716. [32] M.A-S. Seyed, Bayesian and predictive techniques for speaker adaptation, Unpublished PhD Thesis, University of Cambridge, 1996. [33] S.J. Sinclair, Development of an isolated speech digit recognition system based on backpropagation neural networks, Unpublished Masters Thesis, University of Otago, 1996. [34] S.J. Sinclair, C. Watson, The development of the Otago speech database, in: N. Kasabov, G. Coghill (Eds.), Proc. ANNES '95, Dunedin, IEEE Computer Society Press, Los Alamitos, 1995, pp. 294-297. [35] T. Yamakawa, H. Kusanagi, E. Uchino, T. Miki, A new effective algorithm for neo fuzzy neuron model, in: Proc. 5th IFSA World Congress, 1993, pp. 1017-1020. [36] Yamazaki, Research activities on spontaneous speech, in: N. Kasabov, G. Coghill (Eds.), Proc. ANNES '95, Dunedin, IEEE Computer Society Press, Los Alamitos, 1995, pp. 280-283. [37] S. Young, Large vocabulary continuous speech recognition: a review, Internal Report, Cambridge University Engineering Department, 1996

Evolving systems for connectionist-based speech recognition

xv, 519 p. ; 30 cm. Includes bibliographical references. University of Otago department: Information Science. "June 18, 2003".Although studied for several years, speech recognition is still a field that is developing. Recently several important researchers have pointed out areas within the field that need to be addressed. These include robustness to various environments, large or expandable vocabularies, user-friendliness, high recognition accuracy and the ability to recognise continuous speech. The ability to adapt is an important component of a speech recognition system. People new to the system should have the benefits mentioned above. The system should also manage recognition of different speaking rates. Also, novel environments may cause a drop in the system's performance if it lacks robustness or the ability to adapt. A common target for speech recognition algorithms is to detect the presence of speech units, commonly phonemes. This approach involves grouping speech sounds, or phones, into abstract groups that reflect meaning. Recently artificial neural networks have been applied to this task. Nevertheless, uncertainty and ambiguity are inherent in the neural network recognition process. Several novel techniques are proposed to aid in the recognition process, and to help to fulfil the requirements of a successful speech recognition system. The goal of this research is to investigate theories of speech and language processing that are relevant to speech recognition and spoken language understanding. These theories have their foundations in fields such as engineering, computer science, linguistics, natural language processing, psycholinguistics and psychology. An adaptive system is implemented to test the validity and usefulness of such work to the fields of speech recognition and spoken language understanding. For example, the development of abstract structures of the human auditory system and the auditory cortex are investigated, and applied towards better engineering methods for building adaptive speech and language systems. For the implementation of an adaptive speech recognition system, parameters are introduced that can be adjusted either manually or automatically. In this manner, the system can adapt to new speakers and environments. The architecture of the system is modular and hierarchical. Different methods are applied at various levels. For example, artificial neural networks are best suited for low-level processing. A discussion of how errors and uncertainty may be resolved in an unsupervised manner concludes the work. Ideally, the system will adapt to the situation, and the future occurrences of such phenomena may be reduced or eliminated.UnpublishedAbu Hosan, R., Boucher, P., Brugnara, F., De Mori, R., Galler, M., and Snow, M. (1995). Acoustic modeling. Annual report 1995, Centre for Intelligent Machines, McGill University. Barras, C., Caraty, M., and Montacie, C. (1995). Temporal control and training selection for hmm based system. In Eurospeech 95. Bartlett, C. (1992). Regional variation in New Zealand English: The case of Southland. New Zealand English Newsletter, 6:5-15. Bayard, D. and Bartlett, C. (1996). "you must be from Gorrre": Attitudinal effects of Southland rhotic accents and speaker gender on NZE listeners and the question of NZE regional variation. Te Reo, 39:25-45. Bell, A. (1997). Those short front vowels. New Zealand English Journal, 11:3-13. Bengio, Y. (1999). Markovian models for sequential data. Neural Computing Surveys, 2:129-162. Bengio, Y., De Mori, R., and Cardin, R. (1990). Speaker independent speech recognition with neural networks and speech knowledge. In Touretzky, D. E., editor, Advances in Neural Information Processing Systems 2, pages 218-225. Morgan Kaufmann. Bergland, G. D. (1969). A guided tour of the fast fourier transformation. IEEE Spectrum, pages 41-52. Berndt, R. S., Caramazza, A., and Zurif, E. (1983). Language functions: Syntax and semantics. In Segalowitz, S. J., editor, Language Functions and Brain Organization, pages 5-28. Academic Press, New York. Bertoncini, J. B., Bijeljac-Babic, R., Jusczyk, P. W., Kennedy, J. L., and Mehler, J. (1988). An investigation of young infants' perceptual representations of speech sounds. Journal of Experimental Psychology: General, 117(1):21-33. Black, A. W. and Taylor, P. (1994). CHATR: A generic speech synthesis system. In COLING-94, volume 2, pages 983-986, Kyoto, Japan. Black, A. W., Taylor, P., and Caley, R. (1999). The Festival speech synthesis system. System Documentation Edition 1.3, University of Edinburgh. Brennan, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1993). Classification and Regression Trees. The Wadsworth statistics/probability series. Chapman & Hall, New York, NY. Burgess, N. (1994). A constructive algorithm that converges for real-valued input patterns. International Journal of Neural Systems, 5(1):59-66. Campbell, N. (1996). CHATR: A high-definition speech re-sequencing system. In Acoustical Society of America and Acoustical Society of Japan Third Joint Meeting. Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., and Rosen, D. B. (1992). Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks, 3:698-713. Carpenter, G. A. and Tan, A. (1995). Rule extraction: From neural architecture to symbolic representation. Connection Science, 7(1):3-27. Cassidy, S. (1999). Compiling multi-tiered speech databases into the relational model: Experiments with the Emu system. In Proszeky, G., Nemeth, G., and Mandli, J., editors, EuroSpeech, volume 5, pages 2239-2242, Budapest, Hungary. Chang, J. and Glass, J. (1997). Segmentation and modeling in segment-based recognition. In Proc. Eurospeech 1997, pages 1199-1202. Chen, S. and Liao, Y. (1998). Modular recurrent neural networks for mandarin syllable recognition. IEEE Transactions on Neural Networks, 9(6):1430-1441. Clements, G. N. (1990). The role of the sonority cycle in core syllabification. In Kingston, J. and Beckman, M., editors, Papers in Laboratory Phonology I. Cambridge University Press, Cambridge. Cole, R., Hirschman, L., Atlas, L., Beckman, M., Biermann, A., Bush, M., Clements, M., Cohen, J., Garcia, 0., Hanson, B., Hermansky, H., Levinson, S., McKeown, K., Morgan, N., Novick, D., Ostendorf, M., Oviatt, S., Price, P., Silverman, H., Spitz, J., Waibel, A., Weinstein, C., Zahorian, S., and Zue, V. (1995). The challenge of spoken language systems research directions for the nineties. IEEE Transactions on Speech and Audio Processing, 3:1-21. Cole, R. A., Muthusamy, Y., and Fanty, M. A. (1990). The ISOLET spoken letter database. Technical Report 90-004, Oregon Graduate Institute. Craven, M. W. and Shavlik, J. W. (1993). Learning symbolic rules using artificial neural networks. In Proceedings of the Tenth International Conference on Machine Learning, pages 73-80, Amherst , MA. Craven, M. W. and Shavlik, J. W. (1994). Using sampling and queries to extract rules from trained neural networks. In Cohen, W. W. and Hirsh, H., editors, Machine Learning: Proceedings of the Eleventh International Conference, San Francisco, CA. Morgan Kaufmann. Craven, M. W. and Shavlik, J. W. (1997). Using neural networks for data mining. Future Generation Computer Systems, 13(Special Issue on Data Mining):211-229. Craven, M. W. and Shavlik, J. W. (1999). Rule extraction: Where do we go from here? Working Paper 99-1, University of Wisconsin Machine Learning Research Group. Date, C. J. (1990). An Introduction to Database Systems, volume 1. Addison-Wesley, Reading, MA, 5 edition. Davis, S. B. and Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4):357-366. Dehaene-Lambertz, G. and Baillet, S. (1998). A phonological representation in the infant brain. NeuroReport, 9(8):1885-1888. Deverson, T. (1990). `woman's consistancy': A distinctive zero plural in New Zealand English. Te Reo, 33:43-56. Dongxin, X., Taiyi, H., and Zhiwei, L. (1990). A hierarchical structure for feed-forward neural networks and its application to speaker-independent speech recognition. In 10th International Conference on Pattern Recognition. IEEE Computer Society Press. Elman, J. L. (1990). Finding structures in time. Cognitive Sciences, 14:179-211. Esparcia-Alcazar, A. I. and Sharman, K. C. (1996). Evolving recurrent neural network architectures by genetic programming. Technical Report CSC-96009, Centre for Systems and Control, University of Glasgow. Fahlman, S. E. and Lebiere, C. (1990). The cascade-correlation learning architecture. Technical Report CMU-CS-90-100, School of Computer Science, Carnegie Mellon University. Feldkamp, L. A., Puskorius, G. V., Yuan, F., and Davis, Jr., L. I. (1992). Architecture and training of a hybrid neural-fuzzy system. In international conference on Fuzzy Logic Neural Networks, pages 131-134, Iizuka, Japan. Fletcher, J. and Obradovic, Z. (1993). Combining prior symbolic knowledge and constructive neural network learning. Connection Science, 5(3 & 4):365-375. Foldi, N. S., Cicone, M., and Gardner, H. (1983). Pragmatic aspects of communication in brain damaged patients. In Segalowitz, S. J., editor, Language Functions and Brain Organization, pages 55-86. Academic Press, New York. Fowler, C. A., Best, C. T., and McRoberts, G. W. (1990). Young infants' perception of liquid coarticulatory influences on following stop consonants. Perception and Psychophysics, 48(6):559-570. Franzini, M. A., Witbrock, M. J., and Lee, K. (1989). Speaker-independent recognition of connected utterances using recurrent and non-recurrent neural networks. In Proceedings of the International Joint Conference on Neural Networks, Vol. 2, pages 1-6. IEEE. Frean, M. (1990). The Upstart algorithm: A method for constructing and training feedforward neural networks. Neural Computation, 2:198-209. Gallinari, P. (1995). Training of modular neural net systems. In Arbib, M., editor, The Handbook of Brain Theory and Neural Networks, pages 582-585. MIT Press. Garrett, M. (1994). The structure of language processing: Neuropsychological evidence. In Gazzaniga, M., editor, The Cognitive Neurosciences, pages 881-899. MIT press, Cambridge, MA. Ghada, N. A. and Mohamed, A. S. (2000). Evolution of recurrent cascade correlation networks with a distributed collaborative species. In The First IEEE Symposium on Combinations of Evolutionary Computation and Neural Networks, page To Appear, San Antonio, TX,. Giegerich, H. J. (1992). 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Evolving systems for connectionist-based speech recognition

xv, 519 p. ; 30 cm. Includes bibliographical references. University of Otago department: Information Science. "June 18, 2003".Although studied for several years, speech recognition is still a field that is developing. Recently several important researchers have pointed out areas within the field that need to be addressed. These include robustness to various environments, large or expandable vocabularies, user-friendliness, high recognition accuracy and the ability to recognise continuous speech. The ability to adapt is an important component of a speech recognition system. People new to the system should have the benefits mentioned above. The system should also manage recognition of different speaking rates. Also, novel environments may cause a drop in the system's performance if it lacks robustness or the ability to adapt. A common target for speech recognition algorithms is to detect the presence of speech units, commonly phonemes. This approach involves grouping speech sounds, or phones, into abstract groups that reflect meaning. Recently artificial neural networks have been applied to this task. Nevertheless, uncertainty and ambiguity are inherent in the neural network recognition process. Several novel techniques are proposed to aid in the recognition process, and to help to fulfil the requirements of a successful speech recognition system. The goal of this research is to investigate theories of speech and language processing that are relevant to speech recognition and spoken language understanding. These theories have their foundations in fields such as engineering, computer science, linguistics, natural language processing, psycholinguistics and psychology. An adaptive system is implemented to test the validity and usefulness of such work to the fields of speech recognition and spoken language understanding. For example, the development of abstract structures of the human auditory system and the auditory cortex are investigated, and applied towards better engineering methods for building adaptive speech and language systems. For the implementation of an adaptive speech recognition system, parameters are introduced that can be adjusted either manually or automatically. In this manner, the system can adapt to new speakers and environments. The architecture of the system is modular and hierarchical. Different methods are applied at various levels. For example, artificial neural networks are best suited for low-level processing. A discussion of how errors and uncertainty may be resolved in an unsupervised manner concludes the work. Ideally, the system will adapt to the situation, and the future occurrences of such phenomena may be reduced or eliminated.UnpublishedAbu Hosan, R., Boucher, P., Brugnara, F., De Mori, R., Galler, M., and Snow, M. (1995). Acoustic modeling. Annual report 1995, Centre for Intelligent Machines, McGill University. Barras, C., Caraty, M., and Montacie, C. (1995). Temporal control and training selection for hmm based system. In Eurospeech 95. Bartlett, C. (1992). Regional variation in New Zealand English: The case of Southland. New Zealand English Newsletter, 6:5-15. Bayard, D. and Bartlett, C. (1996). "you must be from Gorrre": Attitudinal effects of Southland rhotic accents and speaker gender on NZE listeners and the question of NZE regional variation. Te Reo, 39:25-45. Bell, A. (1997). Those short front vowels. New Zealand English Journal, 11:3-13. Bengio, Y. (1999). Markovian models for sequential data. Neural Computing Surveys, 2:129-162. Bengio, Y., De Mori, R., and Cardin, R. (1990). Speaker independent speech recognition with neural networks and speech knowledge. In Touretzky, D. E., editor, Advances in Neural Information Processing Systems 2, pages 218-225. Morgan Kaufmann. Bergland, G. D. (1969). A guided tour of the fast fourier transformation. IEEE Spectrum, pages 41-52. Berndt, R. S., Caramazza, A., and Zurif, E. (1983). Language functions: Syntax and semantics. In Segalowitz, S. J., editor, Language Functions and Brain Organization, pages 5-28. Academic Press, New York. Bertoncini, J. B., Bijeljac-Babic, R., Jusczyk, P. W., Kennedy, J. L., and Mehler, J. (1988). An investigation of young infants' perceptual representations of speech sounds. Journal of Experimental Psychology: General, 117(1):21-33. Black, A. W. and Taylor, P. (1994). CHATR: A generic speech synthesis system. In COLING-94, volume 2, pages 983-986, Kyoto, Japan. Black, A. W., Taylor, P., and Caley, R. (1999). The Festival speech synthesis system. System Documentation Edition 1.3, University of Edinburgh. Brennan, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1993). Classification and Regression Trees. The Wadsworth statistics/probability series. Chapman & Hall, New York, NY. Burgess, N. (1994). A constructive algorithm that converges for real-valued input patterns. International Journal of Neural Systems, 5(1):59-66. Campbell, N. (1996). CHATR: A high-definition speech re-sequencing system. In Acoustical Society of America and Acoustical Society of Japan Third Joint Meeting. Carpenter, G. A., Grossberg, S., Markuzon, N., Reynolds, J. H., and Rosen, D. B. (1992). Fuzzy ARTMAP: A neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Transactions on Neural Networks, 3:698-713. Carpenter, G. A. and Tan, A. (1995). Rule extraction: From neural architecture to symbolic representation. Connection Science, 7(1):3-27. Cassidy, S. (1999). Compiling multi-tiered speech databases into the relational model: Experiments with the Emu system. In Proszeky, G., Nemeth, G., and Mandli, J., editors, EuroSpeech, volume 5, pages 2239-2242, Budapest, Hungary. Chang, J. and Glass, J. (1997). Segmentation and modeling in segment-based recognition. In Proc. Eurospeech 1997, pages 1199-1202. Chen, S. and Liao, Y. (1998). Modular recurrent neural networks for mandarin syllable recognition. IEEE Transactions on Neural Networks, 9(6):1430-1441. Clements, G. N. (1990). The role of the sonority cycle in core syllabification. In Kingston, J. and Beckman, M., editors, Papers in Laboratory Phonology I. Cambridge University Press, Cambridge. Cole, R., Hirschman, L., Atlas, L., Beckman, M., Biermann, A., Bush, M., Clements, M., Cohen, J., Garcia, 0., Hanson, B., Hermansky, H., Levinson, S., McKeown, K., Morgan, N., Novick, D., Ostendorf, M., Oviatt, S., Price, P., Silverman, H., Spitz, J., Waibel, A., Weinstein, C., Zahorian, S., and Zue, V. (1995). The challenge of spoken language systems research directions for the nineties. IEEE Transactions on Speech and Audio Processing, 3:1-21. Cole, R. A., Muthusamy, Y., and Fanty, M. A. (1990). The ISOLET spoken letter database. Technical Report 90-004, Oregon Graduate Institute. Craven, M. W. and Shavlik, J. W. (1993). Learning symbolic rules using artificial neural networks. In Proceedings of the Tenth International Conference on Machine Learning, pages 73-80, Amherst , MA. Craven, M. W. and Shavlik, J. W. (1994). Using sampling and queries to extract rules from trained neural networks. In Cohen, W. W. and Hirsh, H., editors, Machine Learning: Proceedings of the Eleventh International Conference, San Francisco, CA. Morgan Kaufmann. Craven, M. W. and Shavlik, J. W. (1997). Using neural networks for data mining. Future Generation Computer Systems, 13(Special Issue on Data Mining):211-229. Craven, M. W. and Shavlik, J. W. (1999). Rule extraction: Where do we go from here? Working Paper 99-1, University of Wisconsin Machine Learning Research Group. Date, C. J. (1990). An Introduction to Database Systems, volume 1. Addison-Wesley, Reading, MA, 5 edition. Davis, S. B. and Mermelstein, P. (1980). Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on Acoustics, Speech, and Signal Processing, 28(4):357-366. Dehaene-Lambertz, G. and Baillet, S. (1998). A phonological representation in the infant brain. NeuroReport, 9(8):1885-1888. Deverson, T. (1990). `woman's consistancy': A distinctive zero plural in New Zealand English. Te Reo, 33:43-56. Dongxin, X., Taiyi, H., and Zhiwei, L. (1990). A hierarchical structure for feed-forward neural networks and its application to speaker-independent speech recognition. In 10th International Conference on Pattern Recognition. IEEE Computer Society Press. Elman, J. L. (1990). Finding structures in time. Cognitive Sciences, 14:179-211. Esparcia-Alcazar, A. I. and Sharman, K. C. (1996). Evolving recurrent neural network architectures by genetic programming. Technical Report CSC-96009, Centre for Systems and Control, University of Glasgow. Fahlman, S. E. and Lebiere, C. (1990). The cascade-correlation learning architecture. Technical Report CMU-CS-90-100, School of Computer Science, Carnegie Mellon University. Feldkamp, L. A., Puskorius, G. V., Yuan, F., and Davis, Jr., L. I. (1992). Architecture and training of a hybrid neural-fuzzy system. In international conference on Fuzzy Logic Neural Networks, pages 131-134, Iizuka, Japan. Fletcher, J. and Obradovic, Z. (1993). Combining prior symbolic knowledge and constructive neural network learning. Connection Science, 5(3 & 4):365-375. Foldi, N. S., Cicone, M., and Gardner, H. (1983). Pragmatic aspects of communication in brain damaged patients. In Segalowitz, S. J., editor, Language Functions and Brain Organization, pages 55-86. Academic Press, New York. Fowler, C. A., Best, C. T., and McRoberts, G. W. (1990). Young infants' perception of liquid coarticulatory influences on following stop consonants. Perception and Psychophysics, 48(6):559-570. Franzini, M. A., Witbrock, M. J., and Lee, K. (1989). 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