Speech Communication - Special issue on interactive voice technology for telecommunication applications (IVITA '96)
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Spoken dialogue technology: enabling the conversational user interface
ACM Computing Surveys (CSUR)
Art and Business of Speech Recognition: Creating the Noble Voice
Art and Business of Speech Recognition: Creating the Noble Voice
Incorporating Prior Knowledge into Boosting
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
Construct Algebra: analytical dialog management
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
Empirical methods for evaluating dialog systems
ELDS '01 Proceedings of the workshop on Evaluation for Language and Dialogue Systems - Volume 9
The AT&T spoken language understanding system
IEEE Transactions on Audio, Speech, and Language Processing
A domain-independent statistical methodology for dialog management in spoken dialog systems
Computer Speech and Language
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Building natural language spoken dialogue systems requires large amounts of human transcribed and labeled speech utterances to reach useful operational service performances. Furthermore, the design of such complex systems consists of several manual steps. The User Experience (UE) expert analyzes and defines by hand the system core functionalities: the system semantic scope (call-types) and the dialogue manager strategy that will drive the human–machine interaction. This approach is extensive and error-prone since it involves several nontrivial design decisions that can be evaluated only after the actual system deployment. Moreover, scalability is compromised by time, costs, and the high level of UE know-how needed to reach a consistent design. We propose a novel approach for bootstrapping spoken dialogue systems based on the reuse of existing transcribed and labeled data, common reusable dialogue templates, generic language and understanding models, and a consistent design process. We demonstrate that our approach reduces design and development time while providing an effective system without any application-specific data.