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International Work-Conference on Artificial Neural Networks

IWANN 2001: Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence pp 285–292Cite as

Speech Recognition Using Fuzzy Second-Order Recurrent Neural Networks

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2084))

Abstract

The use of Recurrent Neutral Networks is not so widely extended as Feedforward Neutral Networks. In recent years, these neutral networks are being very studied and they are giving better results than feedforward neural networks in problems related on control, pattern recognition, etc.

In this paper, we present a neural model for speech recognition, whose main characteristic is the little number of neurons that uses and the good results that obtains.

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References

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Author information

Authors and Affiliations

  1. Department of Computer Science and Artificial Intelligence, E.T.S.I. Informática. University of Granada, Avenida de Andalucía, 38, 18071, Granada, Spain

    A. Blanco, M. Delgado, M. C. Pegalajar & I. Requena

Authors
  1. A. Blanco
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  2. M. Delgado
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  3. M. C. Pegalajar
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  4. I. Requena
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Editor information

Editors and Affiliations

  1. Departamento de Inteligencia Artificial Sanda del Rey, Universidad Nacional de Educación a Distancia, s/n., Madrid, 28040, Spain

    José Mira

  2. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, Campus Fuentenueva, 18071, Granada, Spain

    Alberto Prieto

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© 2001 Springer-Verlag Berlin Heidelberg

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Cite this paper

Blanco, A., Delgado, M., Pegalajar, M.C., Requena, I. (2001). Speech Recognition Using Fuzzy Second-Order Recurrent Neural Networks. In: Mira, J., Prieto, A. (eds) Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence. IWANN 2001. Lecture Notes in Computer Science, vol 2084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45720-8_32

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  • DOI: https://doi.org/10.1007/3-540-45720-8_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42235-8

  • Online ISBN: 978-3-540-45720-6

  • eBook Packages: Springer Book Archive

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