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Comparison of neural networks and conventional techniques for automatic recognition of a multilingual speech database

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 540))

Abstract

The paper presents an exhaustive test between the Multilayer Perceptron Fully Connected (MLPFC) trained with backpropagation using the feedback learning rule on one side and three well-established non-NN, standard Isolated Word Recognition (IWR) techniques on the other, using to this aim a multilingual speech database formed by a common vocabulary in three languages: German, Italian and Spanish. This comparative testing has been centered on speaker-independent conditions and two different training subsets have been used. An additional multi-speaker test has been performed on the Spanish vocabulary in order to compare the performances of the MLPFC and backpropagation with feedback learning algorithm with the non-NN methods.

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References

  1. M.H. Kuhn, H. Tomaschewski, H. Ney. "Fast Non-linear Time Alignment for Isolated Word Recognition". Proceedings of the ICASSP, 1981 (pp. 736–740).

    Google Scholar 

  2. L. Rabiner, J. Wilpon. "Considerations in Applying Clustering Techniques to Speaker-Independent Word Recognition". J. Acoust. Soc. An. 66, 1979 (pp. 663–673).

    Google Scholar 

  3. D. O'Shanghnessy. Speech Communication. Addison-Wesley, 1987.

    Google Scholar 

  4. M. Cohen, S. Grossberg, D. Stork. "Recent Developments in a Neural Model of Real-Time Speech Analysis and Synthesis". Proceedings of the IEEE First International Conference on Neural Networks, Vol. IV, 1987 (pp. 443–454).

    Google Scholar 

  5. T. Kohonen, K. Makisara, T. Saramaki. "Phonotopic Maps: Insightful Representation of Phonological Features for Speech Recognition". Proceedings of the 7th International Conference on Pattern Recognition, 1984 (pp. 182–185).

    Google Scholar 

  6. T. Kohonen. "Learning Vector Quantization". Neural Networks Supplement: INNS Abstracts, 1, 303.

    Google Scholar 

  7. J.-H. Pao. Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, 1989.

    Google Scholar 

  8. R. Momenan et al. "Characterization of Tissue from Ultrasound Images". IEEE Control Magazine, vol. 8, No. 3, 1988 (pp. 49–53).

    Google Scholar 

  9. L. Kaufman, P.J. Rousseeuw. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley Interscience, 1990.

    Google Scholar 

  10. D. Maravall, J. Ríos, M. Pérez, A. Carpintero, J. Gómez-Calcerrada. Intermediate Report on the Test of the C-library Algorithms for the Multilingual Speech Database. ESPRIT II — Project 2059, PYGMALION. June 28, 1990.

    Google Scholar 

  11. Ibidem. Final Report on the Test of the C-library Algorithms for the Multilingual Speech Database. ESPRIT II — Project 2059, PYGMALION. December 15, 1990.

    Google Scholar 

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Alberto Prieto

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© 1991 Springer-Verlag Berlin Heidelberg

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Maravall, D., Ríos, J., Pérez-Castellanos, M., Carpintero, A., Gómez-Calcerrada, J. (1991). Comparison of neural networks and conventional techniques for automatic recognition of a multilingual speech database. In: Prieto, A. (eds) Artificial Neural Networks. IWANN 1991. Lecture Notes in Computer Science, vol 540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035917

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  • DOI: https://doi.org/10.1007/BFb0035917

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-54537-8

  • Online ISBN: 978-3-540-38460-1

  • eBook Packages: Springer Book Archive

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