Abstract
This paper is devoted to a neurobiologically plausible approach for the design of speech processing systems. The temporal organization map (TOM) neural net model is a connectionist model for time representation. The definition of a generic neural unit, inspired by the neurobiological model of the cortical column, allows the model to be used for problems including the temporal dimension. In the framework of automatic speech recognition, TOM has been previously tested with conventional techniques of signal processing. An auditory model as front-end processor is now used with TOM, in order to test the efficiency and the accuracy of a physiologically based speech recognition system. Preliminary results axe presented for speaker-dependent and speaker-independent speech recognition experiments. The interest of auditory model is the possibility to develop more valuable processing and communication strategies between TOM and the front-end processor, including afferent and efferent information flow.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
W. A. Ainsworth. Auditory mechanisms for speech perception. In Proc. of Euro speech’95, pages 171–178, Madrid, Spain, 1995.
F. Alexandre, F. Guyot, J. P. Haton, and Y. Burnod. The cortical column: a new processing unit for multilayered networks. Neural networks, 4:15–25, 1991.
F. Berthommier. Intégration neuronale dans le système auditif. Modélisation de réseaux neuronaux temporo-dépendants. PhD thesis, Université Joseph Fourier — Grenoble I, 1992.
Y. Burnod. An adaptive neural network: The cerebral cortex. Masson Paris, 1988.
S. B. Davis and P. Mermelstein. Comparison of parametric representation for monosyllabic word recognition in continuously spoken sentences. IEEE Transactions on acoustics, speech, and signal processing, ASSP-28(4):357–366, 1980.
S. Durand and F. Alexandre. Spatio-temporal mask learning: application to speech recognition. In D. W. Pearson, N. C. Steele, R. F. Albrecht (editors), Artificial Neural Nets and Genetic Algorithms, pages 132–135, Springer-Verlag, Wien, April 1995.
S. Durand and F. Alexandre. Tom, a new temporal neural net architecture for speech signal processing. In IEEE International Conference on Acoustic Speech and Signal Processing, Atlanta, USA, 1996.
J. L. Elman. Finding structure in time. Cognitive Science, 14:179–211, 1990.
B. Fritzke. A growing neural gas network learns topologies. In G. Tesauro, D.S. Touretzky, and T.K. Leen, editors, Advances in Neural Information Processing Systems 7. MIT Press, Cambridge MA, 1995.
Y. Gao, T. Huang, S. Chen, and J. P. Haton. Auditory model based speech processing. In Proc. of ICSLP, pages 73–76, Alberta, Canada, 1992.
T. Kohonen. Self-Organization and Associative Memory. Springer Series in Information Sciences. Springer-Verlag, third edition, 1989.
G. Langner. Periodicity coding in the auditory system. Hearing Research, 60:115–142, 1992.
T. M. Martinetz and K. J. Schulten. A “neural-gas” network learns topologies. In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors, Artificial Neural Network, pages 397–402. North-Holland, Amsterdam, 1991.
S. Seneff. A joint synchrony/mean-rate model of auditory speech processing. Journal of Phonetics, 16:55–76, 1988.
A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, and K J. Lang. Phoneme recognition using time-delay neural networks. IEEE Transaction on Acoustics, Speech and Signal Processing, 37(3):328–339, 1989.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag Wien
About this paper
Cite this paper
Hartwich, E., Alexandre, F. (1998). A Speech Recognition System using an Auditory Model and TOM Neural Network. In: Artificial Neural Nets and Genetic Algorithms. Springer, Vienna. https://doi.org/10.1007/978-3-7091-6492-1_23
Download citation
DOI: https://doi.org/10.1007/978-3-7091-6492-1_23
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-83087-1
Online ISBN: 978-3-7091-6492-1
eBook Packages: Springer Book Archive