Maximum mutual information and conditional maximum likelihood estimations of stochastic regular syntax-directed translation schemes

  • F. Casacuberta
Session: Inference of Stochastic Models 2
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1147)


Formal translations have become of great interest for modeling some Pattern Recognition problems, but they require a stochastic extension in order to deal with noisy and distorted patterns. A Maximum Likelihood estimation has been recently developed for learning the statistical parameters of Stochastic Regular Syntax-Directed Translation Schemes. The goal of this paper is the study of estimation criteria in order to take into account the problem of sparse training data. In particular, these are the Maximum Mutual Information criterion and the Conditional Maximum Likelihood criterion. Some experimental results are reported to compare the three criteria.


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  1. 1.
    L.R.Bahl, F.Jelinek, R.L. Mercer: A Maximum Likelihood Approach to Continuous Speech Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. Vol. 5 (2). pp. 179–196. 1983.Google Scholar
  2. 2.
    L.E.Baum: An Inequality and Associated Maximization Technique in Statistical Estimation for Probabilistic Functions of Markov Processes. Inequalities. Vol. 3, pp. 1–8. 1972.Google Scholar
  3. 3.
    P.F. Brown: The Acoustic-Modeling Problem in Automatic Speech Recognition. Ph.D. dissertation. Dept. Comput. Sci. Carnegi-Mellon Univ. Pittsburgh, PA. 1987.Google Scholar
  4. 4.
    R.Cardin, Y.Normandin,R.DeMori: High Performance Connected Digit Recogntion using Maximum Mutual Information Estimation. IEEE Trans. on Speech and Audio processing. Vol. 2 (2). pp 300–311. 1994.Google Scholar
  5. 5.
    F.Casacuberta: Probabilistic Estimation of Stochastic Regular Syntax-Directed Translation Schemes Proc. of the VI Spanish Symposium on Pattern Recognition and Image Analysis. pp. 201–207. 1995Google Scholar
  6. 6.
    F.Casacuberta. Growth Transformations for Probabilistic Functions of Stochastic Grammars. International Journal on Pattern Recognition and Artificial Intelligence. Vol. 10. No. 3. 1996.Google Scholar
  7. 7.
    F.Casacuberta, la Higuera Some Difficult Problems in Pattern Recognition. To be published. 1996.Google Scholar
  8. 8.
    K.S.Fu: Syntactic Pattern Recognition and Applications. Ed. Prentice-Hall. 1982.Google Scholar
  9. 9.
    R. Gansner, E. Koutsofios, S.C. North, K.P. Vo: A Technique for Drawing Directed Graphs. IEEE Trans. Software Engineering. March, 1993.Google Scholar
  10. 10.
    R.C.Gonzalez, M.G.Thomason: Syntactic Pattern Recognition: an Introduction. Ed. Addison-Wesley. 1978.Google Scholar
  11. 11.
    P.S Goplakrishnan, D.Kanevsky, A.Nadas, D.Nahamoo: An Inequality for rational Functions with Applications to some Statistical Estimation Problems. IEEE Trans. Information Theory. Vol. IT-37 (1). pp 107–113. 1991.CrossRefGoogle Scholar
  12. 12.
    A.Nadas,D.Hahamoo,M.Picherny: On a Model-Robust Training Method for Speech Recognition. Trans. on Acoustic, speech and Signal Processing. Vol. 36 (9), pp 1432–1435. 1988Google Scholar
  13. 13.
    J. Oncina, P. Garcia and E. Vidal: Learning Subsequential Transducers for Pattern Recognition Interpretation Tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence, May 1993.Google Scholar
  14. 14.
    L.R. Rabiner. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77, pp. 257–286, 1989.CrossRefGoogle Scholar
  15. 15.
    E.Vidal, F.Casacuberta, P.Garía: Grammatical Inference and Speech Recognition in New Advances and Trends in Speech Recognition and Coding. NATO ASI Series. pp. 174–191. Springer-Verlag. 1995.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • F. Casacuberta
    • 1
  1. 1.Departamento de Sistemas Informáticos y ComputaciónUniversidad Politécnica de ValenciaSpain

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