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Using Normalized RBF Networks to Map Hand Gestures to Speech

  • S. S. Fels
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 67)

Abstract

Glove-TalkII is a system that translates hand gestures to speech through an adaptive interface. Hand gestures are mapped continuously to 10 control parameters of a parallel formant speech synthesizer. The mapping allows the hand to act as an artificial vocal tract that produces speech in real time. This gives an unlimited vocabulary in addition to direct control of fundamental frequency and volume. Currently, the best version of Glove-TalkII uses several input devices (including a Cyberglove, a 3-space tracker, a keyboard and a foot-pedal), a parallel formant speech synthesizer and 3 neural networks. The gesture-to-speech task is divided into vowel and consonant production by using a mixture of experts architecture where the gating network weights the outputs of a vowel and a consonant neural network. The gating network and the consonant network are trained with examples from the user. The vowel network implements a fixed, user-defined relationship between hand-position and vowel sound and does not require any training examples from the user. Volume, fundamental frequency and stop consonants are produced with a fixed mapping from the input devices. One subject has trained to speak intelligibly with Glove-TalkII. He speaks slowly with speech quality similar to a text-to-speech synthesizer but with far more natural sounding pitch variations.

Keywords

Radial Basis Function Radial Basis Function Network Hand Gesture Hide Unit Output Unit 
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]
    Bell, A.G. (1909), “Making a talking-machine,” Beinn Bhreagh Recorder, pp. 61–72, November.Google Scholar
  2. [2]
    Bridle, J.S. (1990), “Probabilistic interpretation of feedforward classification network outputs, with relationships to statistical pattern recognition,” in Fougelman-Soulie, F. and Herault, J. (Eds.), NATO ASI Series on Systems and Computer Science, Springer-Verlag.Google Scholar
  3. [3]
    Bridle, J.S. (1990), “Training stochastic model recognition algorithms as networks can lead to maximum mutual information estimation of parameters,” in Touretzky, D.S. (Ed.), Neural Information Processing Systems, vol. 2, pp. 111–217, San Mateo, CA, Morgan Kaufmann.Google Scholar
  4. [4]
    Broomhead, D. and Lowe, D. (1988), “Multivariable functional interpolation and adaptive networks,” Complex Systems, vol. 2, pp. 321–355.MathSciNetMATHGoogle Scholar
  5. [5]
    Dudley, H., Riesz, R.R., and Watkins, S.S.A. (1939), “A synthetic speaker,” Journal of the Franklin Institute, vol. 227, no. 6, pp. 739764, June.Google Scholar
  6. [6]
    Fels, S.S. (1994), Glove-TalkIl: Mapping Hand Gestures to Speech Using Neural Networks, Ph.D. thesis, University of Toronto, Toronto, ON, August.Google Scholar
  7. [7]
    Fels, S.S. and Hinton, G. (1993), “Glove-Talk: a neural network interface between a data-glove and a speech synthesizer,” IEEE Transaction on Neural Networks, vol. 4, pp. 2–8.CrossRefGoogle Scholar
  8. [8]
    Fels, S.S. and Hinton, G.E. (1998), “Glove-TalkII: a neural network interface which maps gestures to parallel formant speech synthesizer controls,” IEEE Transactions on Neural Networks, vol. 9, pp. 205–212.CrossRefGoogle Scholar
  9. [9]
    Connectionist Research Group (1990), Xerion Neural Network Simulator Libraries and Man Pages; version 3.183, University of Toronto, Toronto, ON, CANADGoogle Scholar
  10. [10]
    Jones, R.D., Lee, Y.C., Qian, S., Barnes, C.W., Bisset, K.R., Bruce, G.M., Flake, G.W., Lee, K., Lee, L.A., Mead, W.C., O’Rourke, M.K., Poli, I.J., and Thodes, L.E. (1990), “Nonlinear adaptive networks: a little theory, a few applications,” Technical Report LAUR-91–273, Los Alamos National Laboratory.Google Scholar
  11. [11]
    Ladefoged, P. (1982), A Course in Phonetics (2 ed.), Harcourt Brace Javanovich, New York.Google Scholar
  12. [12]
    Lewis, E. (1989), “A ‘C’ implementation of the JSRU text-tospeech system,” Technical report, Computer Science Dept., University of Bristol.Google Scholar
  13. [13]
    Lowry, A., Hall, M.C., and Hughes, P.M. (1989), “Iterative parameter optimization techniques for parallel-formant encoding of speech,” European Conference on Circuit Theory and Design, pp. 537–541.Google Scholar
  14. [14]
    Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986), “Learning internal representations by back-propagating errors,” Nature, vol. 323, pp. 533–536.CrossRefGoogle Scholar
  15. [15]
    Rye, J.M. and Holmes, J.N. (1982), “A versatile software parallel-formant speech synthesizer,” Technical Report JSRU-RR-1016, Joint Speech Research Unit, Malvern, U.K.Google Scholar
  16. [16]
    Von Kempelen, W. Ritter (1970), Mechanismus der menschlichen Sprache nebst Beschreibung einer sprechenden Maschine. Mit einer Einleitung von Herbert E. Brekle und Wolfgang Wild,Stuttgart-Bad Cannstatt F Frommann, Stuttgart. (In German.)Google Scholar
  17. [17]
    Yair, E. and Gersho, A. (1989), “The Boltzmann perceptron network: a multilayered feed-forward network equivalent to the Boltzmann machine,” in Touretzky, D. (Ed.), Advances in Neural Information Processing Systems 1 (NIPS*88), pp. 116–123, San Mateo, Morgan Kaufman Publishers.Google Scholar

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© Springer-Verlag Berlin Heidelberg 2001

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  • S. S. Fels

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