Main Article Content

David Griol
Carlos III of Madrid University
Spain
Jose Manuel Molina
Carlos III of Madrid University
Spain
Vol. 5 No. 4 (2016), Articles, pages 59-69
DOI: https://doi.org/10.14201/ADCAIJ2016545969
Accepted: Nov 15, 2016
Copyright

Abstract

Research in techniques to simulate users has a long history within the fields of language processing, speech technologies and conversational interfaces. In this paper, we describe a technique to develop heterogeneous user models that are able to interact with this kind of interfaces. By means of simulated users, it is possible not only to automatically evaluate the overall operation of a conversational interface, but also to assess the impact of the user responses on the decisions that are selected by the system. The selection of the user responses by the simulated user are based on a statistical model that considers the complete history of the interaction to carry out this selection. We describe this technique and its practical application to measure the influence of the most important user's features characteristics that affect the interaction of the simulated user with the a conversational interface.

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References

Bishop, C. M., 1995. Neural networks for pattern recognition. Oxford University Press.

Chung, G., 2004. Developing a flexible spoken dialog system using simulation. In Proc. of 42nd Annual Meeting of the Association for Computational Linguistics (ACL'04), pages 63–70. Barcelona, Spain. https://doi.org/10.3115/1218955.1218964

Cuayáhuitl, H., Renals, S., Lemon, O., and Shimodaira, H., 2005. Human-Computer Dialogue Simulation Using. https://doi.org/10.1109/asru.2005.1566485

Dutoit, T., 1996. An introduction to text-to-speech synthesis. Kluwer Academic Publishers.

Eckert, W., Levin, E., and Pieraccini, R., 1997. User modeling for spoken dialogue system evaluation. In Proc. of IEEE Automatic Speech Recognition and Understanding Workshop (ASRU'97), pages 80–87. Santa Barbara. https://doi.org/10.1109/asru.1997.658991

Eckert, W., Levin, E., and Pieraccini, R., 1998. Automatic evaluation of spoken dialogue systems. Technical

García, F., Hurtado, L., Sanchis, E., and Segarra, E., 2003. The incorporation of Confidence Measures to Language Understanding. Lecture Notes in Computer Science, 2807:165–172. https://doi.org/10.1007/978-3-540-39398-6_24

Georgila, K., Henderson, J., and Lemon, O., 2005. Learning user simulations for information state update dialogue systems. In Proc. of European Conference on Speech Communications and Technology (Eurospeech'05), pages 893–896. Lisbon, Portugal.

Griol, D., Callejas, Z., López-Cózar, R., and Riccardi, G., 2014. A domain-independent statistical methodology for dialog management in spoken dialog systems. Computer, Speech and Language, 28(3):743–768. https://doi.org/10.1016/j.csl.2013.09.002

Griol, D. and Molina, J., 2015. Modeling Users Emotional State for an Enhanced Human-Machine Interaction. https://doi.org/10.1007/978-3-319-19644-2_30

Wu, W.-L., Lu, R.-Z., Duan, J.-Y., Liu, H., Gao, F., and Chen, Y.-Q., 2010. Spoken language understanding using weakly supervised learning. Computer Speech & Language, 24(2):358–382. https://doi.org/10.1016/j.csl.2009.05.002