Abstract
Current speech recognition systems can be categorized into two broad classes; the knowledge-based approach and the stochastic one. In this paper we present a rule-based method for the recognition of Hungarian vowels. A spectrogram model was used as a front-end module and some acoustic features were extracted (e.g. locations, intensities and shapes of local maxima) from spectrograms by using a genetic algorithm method. On the basis of these features we developed a rule set for the recognition of isolated Hungarian vowels. These rules represented by Prolog clauses were refined by the IMPUT Inductive Logic Programming method.
This work was supported by the grants ESPRIT 20237 and PHARE TDQM 9305-02/1022 (“ILP2/HUN”).
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
References
Alexin, Z., Gyimóthy, T., Boström, H.: Integrating Algorithmic Debugging and Unfolding Transformation in an Interactive Learner in Proceedings of the 12th European Conference on Artificial Intelligence ECAI-96 ed. Wolfgang Wahlster, Budapest, Hungary (1996) 403–407 John Wiley & Son's Ltd. (1996)
Bolla, Kálmán: Magyar fonetikai atlasz, A szegmentálás hangszerkezet elemei Nemzeti Tankönyvkiadó Rt Budapest (1995) in Hungarian
Cooke, M. (eds.): Visual Representations of Speech Signals, Wiley, 1993
Fohr, D., Haton, J., Laprie, Y.: Knowledge-Based Techniques in Acoustic-Phonetic Decoding of Speech: Interest and Limitations, International Journal on Pattern Recognition and Artificial Intelligence Vol. 8, No. 1, (1994) 133–153
Huebener, K., Carson-Berndsen, J.: Phoneme Recognition Using Acoustic Events, Verbmobil Technical Report No. 15, June 1994
Jelasity, M., Dombi, J.: GAS, an Approach on Modeling Species in Genetic Algorithms. Proceedings of the EA'95 (1995)
Kókai, G., Alexin, Z., Kocsis, F.: The IDT System and its Application for Learning Prolog Programs. Proc. of the Sixth International Conference on Artificial Intelligence and Information Control Systems of Robots (AIICSR-94) Smolenice Castle Slovakia September 12–16. (1994) 315–320
Lamel, L. F.: A knowledge-based system for stop consonant identification based on speech spectrogram reading, Computer Speech and Language (1993) 2, 169–191
Lavrač, N., Džeroski, S.: Inductive Logic Programming: Techniques and Applications Ellis Horwood, (1994)
Mammachandran, R. P., Mammone, R. J. (eds.): Modern Methods of Speech Processing, Kluwer Academic, 1995
Mercier, G. et al.: Recognition of speaker-dependent continuous speech with KEAL, IEE Proceedings Vol. 136, Pt.1, No.2, April (1989)
Muggleton, S., De Raedt, L.: Inductive Logic Programming: Theory and methods, Journal of Logic Programming 19 (20) (1994) 629–679
Rabiner, L. R., Schafer, R. W.: Digital Processing of Speech Signals Prentice Hall (1978)
Walbel, A., Lee, K. (eds.): Readings in Speech Recognition, Morgan Kaufmann (1990)
Zue, V. W.: The Use of Speech Knowledge in Automatic Speech Recognition, Proc. IEEE, Vol. 73, No. 11, (1985) 1602–1615
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1997 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Alexin, Z., Csirik, J., Gyimóthy, T., Jelasity, M., Tóth, L. (1997). Learning phonetic rules in a speech recognition system. In: Lavrač, N., Džeroski, S. (eds) Inductive Logic Programming. ILP 1997. Lecture Notes in Computer Science, vol 1297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3540635149_33
Download citation
DOI: https://doi.org/10.1007/3540635149_33
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-63514-7
Online ISBN: 978-3-540-69587-5
eBook Packages: Springer Book Archive