Testing the performance of spoken dialogue systems by means of an artificially simulated user
Artificial Intelligence Review
A statistical approach to spoken dialog systems design and evaluation
Speech Communication
Hybrid reinforcement/supervised learning of dialogue policies from fixed data sets
Computational Linguistics
Data-driven user simulation for automated evaluation of spoken dialog systems
Computer Speech and Language
Technical support dialog systems: issues, problems, and solutions
NAACL-HLT-Dialog '07 Proceedings of the Workshop on Bridging the Gap: Academic and Industrial Research in Dialog Technologies
Hybrid approach to user intention modeling for dialog simulation
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
A comparison between dialog corpora acquired with real and simulated users
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
A statistical user simulation technique for the improvement of a spoken dialog system
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Hybrid user intention modeling to diversify dialog simulations
Computer Speech and Language
User and noise adaptive dialogue management using hybrid system actions
IWSDS'10 Proceedings of the Second international conference on Spoken dialogue systems for ambient environments
Simulation of the grounding process in spoken dialog systems with Bayesian networks
IWSDS'10 Proceedings of the Second international conference on Spoken dialogue systems for ambient environments
Sparse approximate dynamic programming for dialog management
SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Introduction to special issue on machine learning for adaptivity in spoken dialogue systems
ACM Transactions on Speech and Language Processing (TSLP)
Modeling spoken decision support dialogue and optimization of its dialogue strategy
ACM Transactions on Speech and Language Processing (TSLP)
Machine learning for spoken dialogue management: an experiment with speech-based database querying
AIMSA'06 Proceedings of the 12th international conference on Artificial Intelligence: methodology, Systems, and Applications
Learning automata-based approach to learn dialogue policies in large state space
International Journal of Intelligent Information and Database Systems
Sample efficient on-line learning of optimal dialogue policies with kalman temporal differences
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Journal of Ambient Intelligence and Smart Environments - A software engineering perspective on smart applications for AmI
Optimising incremental dialogue decisions using information density for interactive systems
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Inverse reinforcement learning for interactive systems
Proceedings of the 2nd Workshop on Machine Learning for Interactive Systems: Bridging the Gap Between Perception, Action and Communication
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The design of Spoken Dialog Systems cannot be considered as the simple combination of speech processing technologies. Indeed, speech-based interface design has been an expert job for a long time. It necessitates good skills in speech technologies and low-level programming. Moreover, rapid development and reusability of previously designed systems remains uneasy. This makes optimality and objective evaluation of design very difficult. The design process is therefore a cyclic process composed of prototype releases, user satisfaction surveys, bug reports and refinements. It is well known that human intervention for testing is time-consuming and above all very expensive. This is one of the reasons for the recent interest in dialog simulation for evaluation as well as for design automation and optimization. In this paper we expose a probabilistic framework for a realistic simulation of spoken dialogs in which the major components of a dialog system are modeled and parameterized thanks to independent data or expert knowledge. Especially, an Automatic Speech Recognition (ASR) system model and a User Model (UM) have been developed. The ASR model, based on articulatory similarities in language models, provides task-adaptive performance prediction and Confidence Level (CL) distribution estimation. The user model relies on the Bayesian Networks (BN) paradigm and is used both for user behavior modeling and Natural Language Understanding (NLU) modeling. The complete simulation framework has been used to train a reinforcement-learning agent on two different tasks. These experiments helped to point out several potentially problematic dialog scenarios.