A neural probabilistic language model
The Journal of Machine Learning Research
Comparing the utility of state features in spoken dialogue using reinforcement learning
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Inferring tutorial dialogue structure with hidden Markov modeling
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Automatic agenda graph construction from human-human dialogs using clustering method
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Language Resources and Evaluation
Multifunctionality in dialogue
Computer Speech and Language
SWITCHBOARD: telephone speech corpus for research and development
ICASSP'92 Proceedings of the 1992 IEEE international conference on Acoustics, speech and signal processing - Volume 1
Effective handling of dialogue state in the hidden information state POMDP-based dialogue manager
ACM Transactions on Speech and Language Processing (TSLP)
Social correlates of turn-taking style
Computer Speech and Language
Prosodic and temporal features for language modeling for dialog
Speech Communication
Investigating the prosody and voice quality of social signals in scenario meetings
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Directions for research on spoken dialog systems, broadly defined
SDCTD '12 NAACL-HLT Workshop on Future Directions and Needs in the Spoken Dialog Community: Tools and Data
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Models of dialog state are important, both scientifically and practically, but today's best build strongly on tradition. This paper presents a new way to identify the important dimensions of dialog state, more bottom-up and empirical than previous approaches. Specifically, we applied Principal Component Analysis to a large number of low-level prosodic features to find the most important dimensions of variation. The top 20 out of 76 dimensions accounted for 81% of the variance, and each of these dimensions clearly related to dialog states and activities, including turn taking, topic structure, grounding, empathy, cognitive processes, attitude and rhetorical structure.