Trainbot: a spoken dialog system using partially observable Markov decision processes
Trainbot: a spoken dialog system using partially observable Markov decision processes
- Author(s): Weidong Zhou and Baozong Yuan
- DOI: 10.1049/cp.2010.0695
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- Author(s): Weidong Zhou and Baozong Yuan Source: IET 3rd International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2010), 2010 p. 381 – 384
- Conference: IET 3rd International Conference on Wireless, Mobile and Multimedia Networks (ICWMMN 2010)
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- DOI: 10.1049/cp.2010.0695
- ISBN: 978-1-84919-240-8
- Location: Beijing, China
- Conference date: 26-29 Sept. 2010
- Format: PDF
Due to speech recognition and understanding errors, spoken dialog systems have been suffering from inherent uncertainty in the whole conversation. Partially Observable Markov Decision Processes (POMDPs) can provide a principled mathematical framework for modeling the inherent uncertainty in spoken dialogue systems. This Paper describes a dialog system, "Trainbot", which uses a POMDP statistical-based dialog model updating information states and making appropriate dialog strategies in a given situation.
Inspec keywords: interactive systems; Markov processes; speech recognition
Subjects: Speech processing techniques; Markov processes; Speech recognition and synthesis; Markov processes; Speech recognition
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