Abstract
A number of simulated neurons form an artificial neural network. They are connected together in a way similar to biological neural systems. It is the high interconnection rate and the build-in parallelism of these networks that allow completely different processing capabilities in comparison to conventional computer systems. The performance of current pattern recognition systems is far below of humans’ abilities. Artificial neural networks offer the potential of providing new approaches to such problems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Bartkova, K., and Jouvet, D., 1991, Modelization of allophones in a speech recognition system, Proc. XIIth International Conference of Phonetic Science. Vol. 4: 474–477.
Cole, R. A., Fanty,M., Gopalakrishnan, M., and Janssen, D. T., 1991, Speaker-independent name retrieval from spelling using a database of 50,000 names, IEEE Proc ICASSP: 14–17.
Deng, L., and Erler, K., 1992, Structural design of a hidden Markov-model-based recognizer using multivalued phonetic features: Comparison with segmental speech units, J. Acoust. Soc. Am., 92: 3058–3067.
Hampshire, J. B., and Waibel, A. H., 1990, A novel objective function for improved phoneme recognition using time-delay neural networks, IEEE Trans. Neural Networks. Vol. 1, 2: 443–446.
Hebb, D., 1949, “The Organization of Behavior”, John Wiley, New York.
Iso, K., and Watanabe, T., 1990, Speaker-independent word recognition using a neural prediction model, IEEE Proc. ICASS. Vol. 4: 443–446.
Kohonen, T., 1984, “Series in Information Sciences: Vol. 8. Self-Organization and Associative Memory”, Springer-Verlag, Berlin-Heidelberg-New York-Tokyo.
Kohonen, T., 1988, The neural phonetic typewriter, IEEE Computer, 3: 11–22.
Kosko, B. (ed.), 1992, “Neural Networks for Signal Processing”, Prentice-Hall, New Jersey.
Lang, K. J., Waibel, A., and Hinton, G., 1990, A time-delay neural network for isolated word recognition, Neural Networks, 3: 23–29.
Lenning, M., 1990, Putting speech recognition to work in the telephone network, IEEE Computer, 8: 35–41. McCulloch, W., and Pitts, W., 1943, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophysics, 5: 115–121.
Mead, C., 1989, “Analog VLSI and Neural Systems”, Addison-Wesley.
O’Malley, M.H., 1990, Text-to-speech conversion technology, IEEE Computer, 8:17–23. O’Shaunessy, D., 1987, “Speech Communication: Human and Machine”, Addison-Wesley.
Rabiner, L. R., Wilpon, J. G., and Soong, F. K., 1988, High-performance connected-digit recognition using hidden Markov models, IEEE Proc. ICASSP: 119–125.
Ritter, H., Martinetz, T., and Schulten, K., 1990, “Neuronale Netze”, Addison-Wesley, Bonn. Rosenblatt, F., 1958, The perceptron: a probabilistic model for information storage and organization in the brain, Psychol. Review, 65: 386–408.
Rojas, R., 1993, “Theorie der neuronalen Netze”, Springer, Berlin, Heidelberg, New York.
Rummelhart, D., and McClelland, J., 1986, “Parallel Distributed Processing”, MIT Press, Cambridge-Massachusetts.
Strathmeyer, C. R., 1990, Voice in computing: an overview of available technologies, IEEE Computer, 8: 10–15.
Unnikrishnan, K. P., Hopfield, J. J., and Tank, D. W., 1991, Connected-digit speaker-dependent speech recognition using a neural network with time-delayed connections, IEEE Trans. Sig. Proc. Vol. 39, 3: 698–713.
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., and Lang, K., 1987, Phonem recognition using time-delay neural networks, IEEE Trans. ASSP. Vol. 3: 393–404.
Waibel, A., Sawai, H., and Shikano, K., 1989, Consonant and phonem recognition by modular construction of large phonemic time-delay neural networks, Proc. ICASSP: 405–408.
Zimmermann, A., 1992, Mikroelektronische Schaltung zum Aufbau selbstorganisierender Karten aus digitalen Neuronen, Offenlegungsschrift, Deutsches Patentamt DE 4227707 Al, G 06 F 15 /18.
Zimmermann, E., and Lerch, C., 1993, The complex acoustic design of an advertisement call in male mouse lemurs (Microcebus murinus) and sources of its variation, Ethology 93: 211–224.
Zwicker, E., 1982, “Psychoakustik”, Springer, Berlin,Heidelberg,New York,Tokyo.
Zwicker, E., and Fastl, H., 1990, “Psychoacoustics - Facts and Models”, Springer, Berlin, Heidelberg.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1995 Springer Science+Business Media New York
About this chapter
Cite this chapter
Zimmermann, A. (1995). Artificial Neural Networks for Analysis and Recognition of Primate Vocal Communication. In: Zimmermann, E., Newman, J.D., Jürgens, U. (eds) Current Topics in Primate Vocal Communication. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-9930-9_2
Download citation
DOI: https://doi.org/10.1007/978-1-4757-9930-9_2
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4757-9932-3
Online ISBN: 978-1-4757-9930-9
eBook Packages: Springer Book Archive