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Book chapters

  1. Dutoit, T., Couvreur, L., and Bourlard, H. (2009), “How does a dictation machine recognize speech?”, Applied Signal Processing--A MATLAB Approach , pp. 104-148, Springer, MA, 2009 .
  2. Weifeng Li, K. Kumatani, J. John, M. Magimai.-Doss, and H. Bourlard, "A Neural Network based Regression Approach for Recognizing Simultaneous Speech," in Machine Learning for Multimodal Interaction, A. Popescu-Belis and R. Stiefelhagen (Eds.), Springer Berlin, 2008.
  3. Stricker, C., Wagen, J.F., Aradilla, G., Bourlard, H., Hermansky, H., Pinto, J., Henri, P., and Théraulaz, J. (2009), “Intelligent mutli-modal interfaces for mobile applications in hostile environments,” in Human Machine Interaction: Research Results of the MMI Program, Springer-Verlag, 2009.
  4. Bengio, D. and Bourlard, H., (2005), “Multi-Channel Sequence Processing,” Springer Lecture Notes in Computer Sciences (Springer LNAI 3635, 2005), invited talk, Intl. Workshop on Deterministic and Statistical Methods in Machine Learning, Sheffield, September 7-10, 2004.
  5. Magimai-Doss, M. and Bourlard, H. (2005), “On the Adequacy of Baseform Pronunciations and Pronunciation Variants,” Machine Learning for Multimodal Interfaces (First International Workshop, MLMI’04) (Martigny, June 2004), Lecture Notes in Computer Science, Springer, Vol. LNCS 3361, pp. 209-222.
  6. McCowan, I. , Gatica-Perez, D., Bengio, S., Moore, D., and Bourlard, H. (2004), “Towards Computer Understanding of Human Interactions”, Machine Learning for Multimodal Interfaces (First International Workshop, MLMI’04) (Martigny, June 2004), Lecture Notes in Computer Science, Springer, Vol. LNCS 3361, pp. 56-75.
  7. Bourlard, H., Bengio, S., and Weber, K. (2004), “Towards Robust and Adaptive Speech Recognition Models,” in Mathematical Foundations of Speech Processing and Recognition, M. Johnson, S.P. Khudanpur, M. Ostendorf, and R. Rosenfled (Eds.), The IMA Volumes in Mathematics and its Applications, Springer, pp. 169-190.
  8. Morgan, N., Bourlard, H., and Hermansky, H. (2004), “Automatic Speech Recognition: An Auditory Perspective,” in Speech Processing in the Auditory System, S. Greenberg, W. Ainsworth, A. Popper and R. Fay (Eds.), Springer Verlag, New York, pp. 309-338.
  9. McCowan, I., Gatica-Perez, D., Bengio, S., Moore, D., and Bourlard, H. (2003), “Towards Computer Understanding of Human Interactions”, Springer Lecture Notes in Computer Science on “Ambient Intelligence” (Proceedings of European Symposium on Ambient Intelligence, Eindhoven, invited talk, Nov.2-4, 2003), ISSN 0302-9743, Vol. 2875/2003, ISBN: 3-540-20418-0, pp. 235-251.
  10. Beaufays, F., Bourlard, H., Franco H., Morgan, N. (2002), “Speech Recognition Technology,” in The Handbook of Brain Theory and Neural Networks, M. A. Arbib (Ed.), Bradford Books, The MIT Press, pp. 1076-1080.
  11. Bourlard, H, and Bengio, S. (2002), “Hidden Markov Models,” in The Handbook of Brain Theory and Neural Networks, M. A. Arbib (Ed.), Bradford Books, The MIT Press, pp. 528-533.
  12. Bourlard, H., and Morgan, N. (1998), “Hybrid HMM/ANN Systems for Speech Recognition: Overview and New Research Directions,” in Adaptive Processing of Sequences and Data Structures, C.L. Giles and M. Gori (Eds.), Lecture Notes in Artificial Intelligence (1387), Springer Verlag (ISBN 3-540-64341-9), pp. 389-417.
  13. Bourlard, H. and Morgan, N. (1997), “Connectionist Techniques,” in Survey of the State of the Art in Human Language Technology, R. Cole, et al. (Eds.), Cambridge University Press, pp. 356-361.
  14. Konig, Y., Bourlard, H., and Morgan, N. (1995), “REMAP: Recursive Estimation and Maximization of A Posteriori Probabilities---Application to Transition-Based Connectionist Speech Recognition,” in Advances in Neural Information Processing Systems 8, D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.), MIT Press, pp. 388-394.
  15. Bourlard, H. and Morgan, N. (1995), “Hybrid Connectionist Models for Continuous Speech Recognition,” in Automatic Speech and Speaker Recognition, C. H. Lee, K. K.Paliwal, and F. K. Soong (Eds.), Kluwer Academic Publishers, pp. 259-283.
  16. Morgan, N. and Bourlard, H. (1995), “Speech Recognition and Neural Networks: Feature Extraction,” The Handbook of Brain Theory and Neural Networks, M. A. Arbib (Ed.), Bradford Books, MIT Press, pp. 910-913.
  17. Bourlard, H. and Morgan, N. (1995), “Speech Recognition and Neural Networks: Pattern Matching,” The Handbook of Brain Theory and Neural Networks, M. A. Arbib (Ed.), Bradford Books, pp. 913-918, The MIT Press.
  18. Bourlard, H. (1994), “Wernicke: A Neural Network Based, Speaker Independent, Large Vocabulary, Continuous Speech Recognition System,” Advanced Speech Applications --- European Research on Speech Technology}, K. Varghese, S. Pfleger, and J.-P. Lefèvre (Eds.), pp. 300-319, Springer Verlag.
  19. Morgan, N., Bourlard, H., Renals, S., Cohen, M., and Franco. H. (1994), “Hybrid Neural Network/Hidden Markov Model Systems for Continuous Speech Recognition,” in Advances in Pattern Recognition Systems Using Neural Networks Technologies, World Scientific.
  20. Renals, S., Morgan, N., Bourlard, H., Franco, H., and Cohen, M. (1992), “Connectionist Optimization of Tied Mixture Hidden Markov Models,” in Advances in Neural Information Processing Systems 4}, R. P. Lippmann, J. E. Moody, and D. S. Touretzky (Eds.), pp. 167-174, San Mateo, CA: Morgan Kaufmann.
  21. Renals, S., Morgan, N., Bourlard, H. (1991), “Probability Estimation by Feed-Forward Networks in Continuous Speech Recognition,” Proc. of IEEE Workshop on Neural Networks for Signal Processing (Princeton, NJ), B. H. Juang, S. Y. Kung, and C. A. Kann (Eds.), pp. 309-318.
  22. Bourlard, H. and Morgan, N. (1991), “Connectionist Approaches to the Use of Markov Models for Speech Recognition,” in Advances in Neural Information Processing Systems 3, R.P. Lippmann, J.E. Moody, and D.S. Touretzky (Eds.), pp. 213-219, San Mateo, CA: Morgan Kaufmann.
  23. Bourlard, H. and Morgan, N. (1991), “Merging Multilayer Perceptrons and Hidden Markov Models: some Experiments in Continuous Speech Recognition,” in Neural Networks: Advances and Applications, E. Gelenbe (Ed.), Elsevier Science Publishers N. V. (North-Holland), pp. 215-239.
  24. Bourlard, H. (1990), “How Connectionist Models Could Improve Markov Models for Speech Recognition,” in Advanced Neural Computers, R. Eckmiller (Ed.), pp. 247-254, North-Holland.
  25. Bourlard, H. and Morgan, N. (1990), “A Continuous Speech Recognition System Embedding MLP into HMM,” in Advances in Neural Information Processing Systems 2, D.S. Touretzky (Ed.), pp. 186-193, San Mateo, CA: Morgan Kaufmann.
  26. Morgan, N. and Bourlard, H. (1990), “Generalization and Parameter Estimation in Feedforward Nets: Some Experiments,” in Advances in Neural Information Processing Systems 2, D. S. Touretzky (Ed.), pp. 630- 637, San Mateo, CA: Morgan Kaufmann.
  27. Bourlard, H., Morgan, N., and Wellekens, C.J. (1990), “Statistical Inference in Multilayer Perceptrons and Hidden Markov Models with Applications in Continuous Speech Recognition,” in Neurocomputing: Algorithms, Architectures and Applications, F., Fogelman and J. Hérault (Eds.), NATO ASI Series, vol. F68, Springer-Verlag, pp. 217-226.
  28. Bourlard, H. and Wellekens, C.J. (1989), “Links between Markov Models and Multilayer Perceptrons,” Advances in Neural Information Processing Systems 1, D.S.Touretzky (Ed.), pp. 502-510, San Mateo, CA: Morgan Kauffmann.
  29. Bourlard, H., Kamp, Y., Ney, H., and Wellekens, C.J. (1985), “Speaker Dependent Connected Speech Recognition via Dynamic Programming and Statistical Methods”, in Speech and Speaker Recognition, M.R. Schroeder (Ed.), pp.115-148, Karger (Basel).