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Evolution of Communicating Individuals

  • Leonardo Bocchi
  • Sara Lapi
  • Lucia Ballerini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6024)

Abstract

Verbal communication between individuals requires the parallel evolution of a vocal system capable of emitting different sounds and of an auditory system able to recognize each vocal pattern. In this work we present the evolution of a population of twins where the selection pressure is based on the ability of learning a communication pattern which allows verbal transmission of information. The fitness of each pair of twins (i.e. individuals having the same genotype) is based on the percentage of correct recognition of the perceived sounds. Results indicate the evolved communication system, in absence of noise, rapidly evolves and reaches almost 100% correct classifications, while, even in presence of a strong noise either in the channel, or in the sound generation parameters, the system can obtain a very good performance (approximately 80% correct classifications in the worst case).

Keywords

Auditory System Vocal Tract Triangular Pulse Additive White Noise Vocal Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Beautemps, D., Badin, P., Laboissière, R.: Deriving vocal-tract area functions from midsagittal profiles and formant frequencies: A new model for vowels and fricative consonants based on experimental data. Speech Communication 16(1), 27–47 (1995)CrossRefGoogle Scholar
  2. 2.
    Bonada, J., Loscos, A., Cano, P., Serra, X., Kenmochi, H.: Spectral approach to the modeling of the singing voice. In: Proceedings of 111th AES Convention (2001)Google Scholar
  3. 3.
    Christophe, B.D., Henrich, N.: The voice source as a causal/anticausal linear filter. In: Proc. ISCA ITRW VOQUAL 2003, pp. 15–19 (2003)Google Scholar
  4. 4.
    Clément, P., Hans, S., Hartl, D., Maeda, S., Vaissière, J., Brasnu, D.: Vocal tract area function for vowels using three-dimensional magnetic resonance imaging. a preliminary study. Journal of Voice 21, 522–530 (2007)CrossRefGoogle Scholar
  5. 5.
    Deller, J.J.R., Hansen, J.H.L., Proakis, J.G.: Discrete-Time Processing of Speech Signals, 2nd edn. Wiley-IEEE Press, Chichester (1999)Google Scholar
  6. 6.
    Kob, M., Alhäuser, N., Reiter, U.: Time-domain model of the singing voice (1999)Google Scholar
  7. 7.
    Macon, M.W., Clements, M.A.: Sinusoidal modeling and modification of unvoiced speech. IEEE Transactions on Speech and Audio Processing, 557–560 (1997)Google Scholar
  8. 8.
    Maeda, S.: A digital simulation method of the vocal-tract system. Speech Communication 1, 199–229 (1982)CrossRefGoogle Scholar
  9. 9.
    Narayanan, S., Alwan, A.: Noise source models for fricative consonants. IEEE Transactions on Speech and Audio Processing 8, 2000 (2000)Google Scholar
  10. 10.
    Rabiner, L., Schafer, R.: Digital Processing of Speech Signals. Prentice Hall, Englewood Cliffs (1978)Google Scholar
  11. 11.
    Rabiner, L.R., Juang, B.H.: Fundamentals of Speech Recognition. Prentice Hall, Englewood Cliffs (1993)Google Scholar
  12. 12.
    Story, B., Titze, I.: Parameterization of vocal tract area functions by empirical orthogonal modes. J. Phonetics 26, 223–260 (1998)CrossRefGoogle Scholar
  13. 13.
    Wakita, H.: Direct estimation of the vocal tract shape by inverse filtering of acoustic speech waveforms. IEEE Transactions on Audio and Electroacoustics 21(5), 417–427 (1973)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Leonardo Bocchi
    • 1
  • Sara Lapi
    • 1
  • Lucia Ballerini
    • 2
  1. 1.Dept. of Electronics and TelecommunicationsUniv. of FlorenceItaly
  2. 2.School of InformaticsUniversity of EdinburghUK

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