Abstract
The new Interactive Pattern Recognition (IPR) framework has been recently proposed. This proposal lets a human interact with a Pattern Recognition system allowing the system to learn from the interaction as well as adapt it to the human behavior. The aim of this paper is to apply the principles of IPR to the design of Spoken Dialog Systems (SDS). We propose a new formulation to present SDS as an IPR problem. To this end some extensions to the IPR approach are proposed. Additionally a user model based on the IPR paradigm is also defined. We applied the proposed formulation to compose a preliminary graphical model that has been experimentally developed to deal with a Spanish dialog task. An initial maximum likelihood strategy for the dialog manager actions along with a stochastic simulation of user behavior have allowed to get new dialogs. The preliminary evaluation of these results allowed us to consider this formulation as a promising framework to deal with SDS.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Toselli, A.H., Vidal, E., Casacuberta, F. (eds.): Multimodal Interactive Pattern Recognition and Applications. Springer (2011)
Lemon, O., Pietquin, O.: Machine learning for spoken dialogue systems. In: Proceedings of the 10th European Conference on Speech Communication and Technology. Interspeech, Antwerp, Belgium, August 27-31, pp. 2685–2688 (2007)
Griol, D., Hurtado, L.F., Segarra, E., Sanchis, E.: A statistical approach to spoken dialog systems design and evaluation. Speech Communication 50, 666–682 (2008)
Meng, H., Wai, C., Pieraccini, R.: The use of belief networks for mixed-initiative dialog modeling. IEEE Trans. Speech and Audio Processing 11(6), 757–773 (2003)
Williams, J.D., Young, S.: Partially observable markov decision processes for spoken dialog systems. Computer Speech and Language 21, 393–422 (2007)
Sarigaya, R., Gao, Y., Picheney, M.: A comparison of rule-based and statistical methods for semantic language modeling and confidence measurement. In: Proceedings of the Human Language Technology Conference. North American Chapter of the Association for Computational Linguistics Annual Meeting. HLT-NAACL, Boston, pp. 65–68 (2007)
Cuayáhuitl, H., Renals, S., Lemon, O., Shimodaira, H.: Evaluation of a hierarchical reinforcement learning spoken dialogue system. Computer, Speech and Language 25, 395–429 (2010)
Williams, J.D.: Incremental partiten recombination for efficient tracking of multiple dialog states. In: Proceedings of International Conference on Acoustics, Speech and Signal Processing (ICASSP), Dallas, USA (2010)
Hajdinjak, M., Mihleic̃, F.: The pradise evaluation framework: Issues and findings. Computational Linguistics 32(2), 263–272 (2006)
Lee, S., Eskenazi, M.: An unsupervised approach to user simulation: toward self-improving dialog systems. In: Proceedings of the SIGDIAL Conference, Seoul, Korea, pp. 50–59 (July 2012)
Benedí, J., Lleida, E., Varona, A., Castro, M., Galiano, I., Justo, R., López, I., Miguel, A.: Design and acquisition of a telephone spontaneous speech dialogue corpus in Spanish: DIHANA. In: Proceedings of LREC 2006, Genoa (May 2006)
Alcácer, N., Benedí, J.M., Blat, F., Granell, R., Martínez, C.D., Torres, F.: Acquisition and labelling of a spontaneous speech dialogue corpus. In: Proceeding of 10th International Conference on Speech and Computer (SPECOM), Patras, Greece, pp. 583–586 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Torres, M.I., Benedí, J.M., Justo, R., Ghigi, F. (2012). Modeling Spoken Dialog Systems under the Interactive Pattern Recognition Framework. In: Gimel’farb, G., et al. Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2012. Lecture Notes in Computer Science, vol 7626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34166-3_57
Download citation
DOI: https://doi.org/10.1007/978-3-642-34166-3_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34165-6
Online ISBN: 978-3-642-34166-3
eBook Packages: Computer ScienceComputer Science (R0)