MACHINE LEARNING OF WORD PRONUNCIATION: THE CASE AGAINST ABSTRACTION

6th European Conference on Speech Communication and Technology, EUROSPEECH 1999

Volumn , Issue , 1999, Pages 2123-2126

  Busser, Bertjan ^a   Daelemans, Walter ^a   van den Bosch, Antal ^a  

^a TILBURG UNIVERSITY   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ABSTRACTING; BACKPROPAGATION; SPEECH COMMUNICATION;

CLASSIFICATION TASKS; EMPIRICAL STUDIES; GENERALIZATION ACCURACY; INDUCTIVE MACHINE LEARNING; LEARNING METHODS; MACHINE LEARNING ALGORITHMS; MACHINE-LEARNING; TASK CLASSIFY; WORD CONTEXTS; WORD PRONUNCIATION;

DECISION TREES;

EID: 85032404317     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. D. W. Aha, D. Kibler, and M. Albert. Instance-based learning algorithms. Machine Learning, 6:37-66, 1991.
2. R. H. Baayen, R. Piepenbrock, and H. van Rijn. The CELEX lexical data base on CD-ROM. Linguistic Data Consortium, Philadelphia, PA, 1993.
3. W. Daelemans and A. Van den Bosch. Generalisation performance of backpropagation learning on a syllabification task. In M. F. J. Drossaers and A. Nijholt, editors, Proc. of TWLT3: Connectionism and Natural Language Processing, pages 27-37, Enschede, 1992. Twente University.
4. W. Daelemans and A. Van den Bosch. A neural network for hyphenation. In I. Aleksander and J. Taylor, editors, Artificial Neural Networks 2, volume 2, pages 1647-1650, Amsterdam, 1992. North-Holland.
5. W. Daelemans, A. Van den Bosch, and G. Durieux. Toward inductive lexicons: a case study. In Proceedings of the LREC Workshop on Adapting Lexical and Corpus Resources to Sublanguages and Applications, pages 29-35, Granada, Spain, 1998.
6. W. Daelemans, A. Van den Bosch, and A. Weijters. igtree: using trees for compression and classification in lazy learning algorithms. Artificial Intelligence Review, 11:407-423, 1997.
7. W. Daelemans, A. Van den Bosch, and J. Zavrel. Forgetting exceptions is harmful in language learning. Machine Learning, 11:11-43, 1999.
8. T. G. Dietterich, H. Hild, and G. Bakiri. A comparison of id3 and backpropagation for English text-to-speech mapping. Machine Learning, 19(1):5-28, 1995.
9. P. Domingos. Unifying instance-based and rule-based induction. Machine Learning, 24:141-168, 1996.
10. P. E. Hart. The condensed nearest neighbor rule. IEEE Transactions on Information Theory, 14:515-516, 1968.
11. J.R. Quinlan. c4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.
12. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning internal representations by error propagation. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing: Explorations in the Microstructure of Cognition, volume 1: Foundations, pages 318-362. The MIT Press, Cambridge, MA, 1986.
13. S. Salzberg. Learning with nested generalised exemplars. Kluwer Academic Publishers, Norwell, MA, 1990.
14. S. Salzberg. A nearest hyperrectangle learning method. Machine Learning, 6:277-309, 1991.
15. T. J. Sejnowski and C. S. Rosenberg. Parallel networks that learn to pronounce English text. Complex Systems, 1:145-168, 1987.
16. A. Van den Bosch. Learning to pronounce written words: A study in inductive language learning. PhD thesis, Universiteit Maastricht, 1997.
17. A. Van den Bosch. Careful abstraction from instance families in memory-based language learning. Journal of Experimental and Theoretical Artificial Intelligence, 1999. To appear.
18. A. Van den Bosch and W. Daelemans. A distributed, yet symbolic model of text-to-speech processing. to appear in P. Broeder and J. Murre (Eds.), Models of language learning: inductive and deductive approaches. Cambridge, MA: MIT Press, 1997., forthcoming.
19. A. Weijters. A simple look-up procedure superior to NETtalk? In Proceedings of the International Conference on Artificial Neural Networks - ICANN-91, Espoo, Finland, 1991.