A neural clustering algorithm for estimating visible articulatory trajectory

  • Fabio Vignoli
  • Sergio Curinga
  • Fabio Lavagetto
Poster Presentations 3 Sensory Processing
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1112)


The bimodal acoustic-visual nature of speech establishes sound correlations between its audio component and the corresponding articulatory information associated to the time-varying geometry of the vocal tract. In this paper we propose an estimation structure consisting of a simplified Time-Delay Neural Network (TDNN) working on 4–5 dimensional cepstrum trajectories provided by a preceding clusterization layer based on a Self Organizing Map (SOM). The use of this pre-processing layer has allowed an effective non-linear clusterization of cepstrum vectors thus simplifying of one order the complexity of the resulting system while maintaining unchanged the global estimation performances. The achieved results are shown in terms estimation precision and robustness with reference to previously published results.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    F.Lavagetto,”Converting Speech into Lip Movements: A Multimedia Telephone for Hard of Hearing People” IEEE Trans. on RE, Vol.3, n.1, 1995, pp. 90–102.Google Scholar
  2. 2.
    A.Q. Summerfield, ”Use of Visual Information for Phonetic Perception”, Phonetica, Vol.36, pp.314–331, 1979.Google Scholar
  3. 3.
    E. Owens, B. Blazek, ”Visems Observed by Hearing-Impaired and Normal-Hearing Adult Viewers”, Journal of Speech and Hearing Research, vol.28, pp.381–393, 1985.Google Scholar
  4. 4.
    C.A. Fowler ”Coarticulation and Theories of Extrinsic Timing”, Journal of Phonetics, 1980.Google Scholar
  5. 5.
    O. Fujimura ”Elementary gestures and temporal organization. What does an articulatory constraint means?” in The cognitive representation of speech, North Holland Amsterdam, pp. 101–110, 1981.Google Scholar
  6. 6.
    A.P. Benguerel, M.K. Pichora-Fuller, ”Coarticulation Effects in Lipreading”, Journal of Speech and Hearing Research, Vol.25, pp.600–607, 1982.Google Scholar
  7. 7.
    S. Morishima, H. Harashima, ”A Media Conversion from Speech to Facial Image for Intelligent Man-Machine Interface”, IEEE Journal on Sel. Areas in Comm.,vol.9, N.4, pp. 594–600, 1991.Google Scholar
  8. 8.
    B.P. Yuhas, M.H. Goldstein Jr. and T.J. Sejnowski, ”Integration of Acoustic and Visual Speech Signal Using Neural Networks”, IEEE Communications Magazine, pp. 65–71, 1989.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Fabio Vignoli
    • 1
  • Sergio Curinga
    • 1
  • Fabio Lavagetto
    • 1
  1. 1.DIST University of GenovaGenovaItaly

Personalised recommendations