An Online Algorithm for Hierarchical Phoneme Classification

  • Ofer Dekel
  • Joseph Keshet
  • Yoram Singer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3361)

Abstract

We present an algorithmic framework for phoneme classification where the set of phonemes is organized in a predefined hierarchical structure. This structure is encoded via a rooted tree which induces a metric over the set of phonemes. Our approach combines techniques from large margin kernel methods and Bayesian analysis. Extending the notion of large margin to hierarchical classification, we associate a prototype with each individual phoneme and with each phonetic group which corresponds to a node in the tree. We then formulate the learning task as an optimization problem with margin constraints over the phoneme set. In the spirit of Bayesian methods, we impose similarity requirements between the prototypes corresponding to adjacent phonemes in the phonetic hierarchy. We describe a new online algorithm for solving the hierarchical classification problem and provide worst-case loss analysis for the algorithm. We demonstrate the merits of our approach by applying the algorithm to synthetic data and as well as speech data.

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References

  1. 1.
    Deller, J., Proakis, J., Hansen, J.: Discrete-Time Processing of Speech Signals. Prentice-Hall, Englewood Cliffs (1987)Google Scholar
  2. 2.
    Rabiner, L.R., Schafer, R.W.: Digital Processing of Speech Signals. Prentice-Hall, Englewood Cliffs (1978)Google Scholar
  3. 3.
    Robinson, A.J.: An application of recurrent nets to phone probability estimation. IEEE Transactions on Neural Networks 5, 298–305 (1994)CrossRefGoogle Scholar
  4. 4.
    Clarkson, P., Moreno, P.: On the use of support vector machines for phonetic classification. In: Proceedings of the International Conference on Acoustics, Speech and Signal Processing 1999, Phoenix, Arizona (1999)Google Scholar
  5. 5.
    Salomon, J.: Support vector machines for phoneme classification. Master’s thesis, University of Edinburgh (2001)Google Scholar
  6. 6.
    Koller, D., Sahami, M.: Hierarchically classifying docuemnts using very few words. In: Machine Learning: Proceedings of the Fourteenth International Conference, pp. 171–178 (1997)Google Scholar
  7. 7.
    McCallum, A.K., Rosenfeld, R., Mitchell, T.M., Ng, A.Y.: Improving text classification by shrinkage in a hierarchy of classes. In: Proceedings of ICML 1998, pp. 359–367 (1998)Google Scholar
  8. 8.
    Weigend, A.S., Wiener, E.D., Pedersen, J.O.: Exploiting hierarchy in text categorization. Information Retrieval 1, 193–216 (1999)CrossRefGoogle Scholar
  9. 9.
    Dumais, S.T., Chen, H.: Hierarchical classification of Web content. In: Proceedings of SIGIR 2000, pp. 256–263 (2000)Google Scholar
  10. 10.
    Katz, S.: Estimation of probabilities from sparsedata for the language model component of a speech recognizer. IEEE Transactions on Acoustics, Speech and Signal Processing (ASSP) 35, 400–440 (1987)CrossRefGoogle Scholar
  11. 11.
    Vapnik, V.N.: Statistical Learning Theory. Wiley, Chichester (1998)MATHGoogle Scholar
  12. 12.
    Crammer, K., Dekel, O., Shalev-Shwartz, S., Singer, Y.: Online passive aggressive algorithms. Advances in Neural Information Processing Systems 16 (2003)Google Scholar
  13. 13.
    Herbster, M.: Learning additive models online with fast evaluating kernels. In: Proceedings of the Fourteenth Annual Conference on Computational Learning Theory, pp. 444–460 (2001)Google Scholar
  14. 14.
    Kivinen, J., Warmuth, M.K.: Exponentiated gradient versus gradient descent for linear predictors. Information and Computation 132, 1–64 (1997)MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Cesa-Bianchi, N., Conconi, A., Gentile, C.: On the generalization ability of on-line learning algorithms. IEEE Transactions on Information Theory (2004) (to appear)Google Scholar
  16. 16.
    Weston, J., Watkins, C.: Support vector machines for multi-class pattern recognition. In: Proceedings of the Seventh European Symposium on Artificial Neural Networks (1999)Google Scholar
  17. 17.
    Censor, Y., Zenios, S.: Parallel Optimization: Theory, Algorithms, and Applications. Oxford University Press, New York (1997)MATHGoogle Scholar
  18. 18.
    Lemel, L., Kassel, R., Seneff, S.: Speech database development: Design and analysis. In: Proc. DARPA Speech Recognition Workshop, Report no. SAIC-86/1546 (1986)Google Scholar
  19. 19.
    ETSI Standard, ETSI ES 201 108 (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ofer Dekel
    • 1
  • Joseph Keshet
    • 1
  • Yoram Singer
    • 1
  1. 1.School of Computer Science and EngineeringThe Hebrew UniversityJerusalemIsrael

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