Two people walk into a bar: dynamic multi-party social interaction with a robot agent
Proceedings of the 14th ACM international conference on Multimodal interaction
IrisTK: a statechart-based toolkit for multi-party face-to-face interaction
Proceedings of the 14th ACM international conference on Multimodal interaction
Movie-DiC: a movie dialogue corpus for research and development
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Short Papers - Volume 2
Optimising incremental generation for spoken dialogue systems: reducing the need for fillers
INLG '12 Proceedings of the Seventh International Natural Language Generation Conference
An adaptive dialogue system for assessing post traumatic stress disorder
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
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The past decade has seen a revolution in the field of spoken dialogue systems. As in other areas of Computer Science and Artificial Intelligence, data-driven methods are now being used to drive new methodologies for system development and evaluation. This book is a unique contribution to that ongoing change. A new methodology for developing spoken dialogue systems is described in detail. The journey starts and ends with human behaviour in interaction, and explores methods for learning from the data, for building simulation environments for training and testing systems, and for evaluating the results. The detailed material covers: Spoken and Multimodal dialogue systems, Wizard-of-Oz data collection, User Simulation methods, Reinforcement Learning, and Evaluation methodologies. The book is a research guide for students and researchers with a background in Computer Science, AI, or Machine Learning. It navigates through a detailed case study in data-driven methods for development and evaluation of spoken dialogue systems. Common challenges associated with this approach are discussed and example solutions are provided. This work provides insights, lessons, and inspiration for future research and development not only for spoken dialogue systems in particular, but for data-driven approaches to human-machine interaction in general.