Abstract
The increasing number of spoken dialog systems calls for efficient approaches for their development and testing. Our goal is the minimization of hand-crafted resources to maximize the portability of this evaluation environment across spoken dialog systems and domains. In this paper we discuss the user simulation technique which allows us to learn general user strategies from a new corpus. We present this corpus, the VOICE Awards human-machine dialog corpus, and show how it is used to semi-automatically extract the resources and knowledge bases necessary in spoken dialog systems, e.g., the ASR grammar, the dialog classifier, the templates for generation, etc.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Scheffler, T., Roller, R., Reithinger, N.: Speecheval – evaluating spoken dialog systems by user simulation. In: Proceedings of the 6th IJCAI Workshop on Knowledge and Reasoning in Practical Dialogue Systems, Pasadena, CA, pp. 93–98 (2009)
Alexandersson, J., Heisterkamp, P.: Some notes on the complexity of dialogues. In: Proceedings of the 1st Sigdial Workshop on Discourse and Dialogue, Hong Kong, vol. 10, pp. 160–169 (2000)
Ai, H., Litman, T., Litman, D.: Comparing user simulation models for dialog strategy learning. In: Proceedings of NAACL/HLT 2007, Rochester, NY, pp. 1–4 (2007)
López-Cózar, R., de la Torre, A., Segura, J., Rubio, A.: Assessment of dialog systems by means of a new simulation technique. Speech Communication 40, 387–407 (2003)
Schatzmann, J., Weilhammer, K., Stuttle, M., Young, S.: A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies. The Knowledge Engineering Review (2006)
Georgila, K., Henderson, J., Lemon, O.: Learning user simulations for information state update dialogue systems. In: Proceedings of the 9th European Conference on Speech Communication and Technology (Eurospeech), Lisbon, Portugal (2005)
Lemon, O., Pietquin, O.: Machine learning for spoken dialogue systems. In: Proceedings of Interspeech (2007)
Rieser, V., Lemon, O.: Learning dialogue strategies for interactive database search. In: Proceedings of Interspeech (2007)
AMIDA: Deliverable D5.2: Report on multimodal content abstraction. Technical report, DFKI GmbH, ch. 4 (2007)
Germesin, S., Becker, T., Poller, P.: Determining latency for on-line dialog act classification. In: MLMI 2008 (September 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Scheffler, T., Roller, R., Reithinger, N. (2009). Semi-automatic Creation of Resources for Spoken Dialog Systems. In: Mertsching, B., Hund, M., Aziz, Z. (eds) KI 2009: Advances in Artificial Intelligence. KI 2009. Lecture Notes in Computer Science(), vol 5803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04617-9_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-04617-9_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04616-2
Online ISBN: 978-3-642-04617-9
eBook Packages: Computer ScienceComputer Science (R0)