Abstract
Neural networks (NN) have been intensively used for speech processing (Morgan and Scofield, 1991). This paper describes a series of experiments on using a single Kohonen Self Organizing Map (KSOM), hierarchically organised KSOM, a backpropagation- type neural network with fuzzy inputs and outputs, and a fuzzy system, for continuous speech recognition. Experiments with different non-linear transfonnations on the signal before using a KSOM has been done. The results obtained by using different techniques on the case study of phonemes in Bulgarian language are compared.
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References
Morgan, D. and Scofield, C. (1991) Neural networks and Speech processing. Kluwer Academic Publishers
Kasabov, N. (1993) Learning fuzzy production rules for approximate reasoning in connectionist production systems, in: S.Gielen and B.Kappen (Eds) Proceedings of ICANN’93, Springer Verlag, 337–342
Kasabov, N., Nikovski, D. and E.Peev (1993) Speech recognition based on Kohonen Self Organizing Feature Maps and Hybrid Connectionist Systems, in: N.Kasabov (Ed) Artificial Neural Networks and Expert Systems, IEEE Computer Society Press, Los Alamitos, 113–117
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© 1994 Springer-Verlag London Limited
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Kasabov, N., Peev, E. (1994). Phoneme Recognition with Hierarchical Self Organised Neural Networks and Fuzzy Systems - A Case Study. In: Marinaro, M., Morasso, P.G. (eds) ICANN ’94. ICANN 1994. Springer, London. https://doi.org/10.1007/978-1-4471-2097-1_47
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DOI: https://doi.org/10.1007/978-1-4471-2097-1_47
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Online ISBN: 978-1-4471-2097-1
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