ASR system modeling for automatic evaluation and optimization of dialogue

Olivier Pietquin, Steve Renals

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Though the field of spoken dialogue systems has developed quickly in the last decade, rapid design of dialogue strategies remains uneasy. Several approaches to the problem of automatic strategy learning have been proposed and aie use of Reinforcement Learning introduced by Levin and Pieraccini is becoming part of the state of the art in this area. However, the quality of the strategy learned by the system depends on the definition of the optimization criterion and on the accuracy of aie environment model. In this paper, we propose to bring a model of an ASR system in the simulated environment in order to enhance the learned strategy. To do so, we introduced recognition error rates and confidence levels produced by ASR systems in the optimization criterion.
Original languageEnglish
Title of host publicationProceedings of the 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing
Subtitle of host publicationICASSP 2002
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages45-48
Volume1
ISBN (Print)0-7803-7402-9
DOIs
Publication statusPublished - May 2002
Event2002 IEEE International Conference on Acoustics, Speech, and Signal Processing - Orlando, FL, United States
Duration: 13 May 200217 May 2002

Conference

Conference2002 IEEE International Conference on Acoustics, Speech, and Signal Processing
Country/TerritoryUnited States
CityOrlando, FL
Period13/05/0217/05/02

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