A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Speech Communication - Special issue on interactive voice technology for telecommunication applications (IVITA '96)
Automatic Speech Recognition: The Development of the Sphinx Recognition System
Automatic Speech Recognition: The Development of the Sphinx Recognition System
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
Partially observable Markov decision processes for spoken dialog systems
Computer Speech and Language
Predicting the quality and usability of spoken dialogue services
Speech Communication
Automatically training a problematic dialogue predictor for a spoken dialogue system
Journal of Artificial Intelligence Research
Analysis of listening-oriented dialogue for building listening agents
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling user satisfaction with Hidden Markov Model
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
Modeling and predicting quality in spoken human-computer interaction
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Towards quality-adaptive spoken dialogue management
SDCTD '12 NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data
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This paper proposes a novel approach for predicting user satisfaction transitions during a dialogue only from the ratings given to entire dialogues, with the aim of reducing the cost of creating reference ratings for utterances/dialogue-acts that have been necessary in conventional approaches. In our approach, we first train hidden Markov models (HMMs) of dialogue-act sequences associated with each overall rating. Then, we combine such rating-related HMMs into a single HMM to decode a sequence of dialogue-acts into state sequences representing to which overall rating each dialogue-act is most related, which leads to our rating predictions. Experimental results in two dialogue domains show that our approach can make reasonable predictions; it significantly outperforms a baseline and nears the upper bound of a supervised approach in some evaluation criteria. We also show that introducing states that represent dialogue-act sequences that occur commonly in all ratings into an HMM significantly improves prediction accuracy.