Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Influencing group participation with a shared display
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
High frequency word entrainment in spoken dialogue
HLT-Short '08 Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers
Computational measures for language similarity across time in online communities
ACTS '09 Proceedings of the HLT-NAACL 2006 Workshop on Analyzing Conversations in Text and Speech
Computational modelling of structural priming in dialogue
NAACL-Short '06 Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers
It's not you, it's me: detecting flirting and its misperception in speed-dates
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Entrainment in speech preceding backchannels
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work
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Speech style accommodation refers to shifts in style that are used to achieve strategic goals within interactions. Models of stylistic shift that focus on specific features are limited in terms of the contexts to which they can be applied if the goal of the analysis is to model socially motivated speech style accommodation. In this paper, we present an unsupervised Dynamic Bayesian Model that allows us to model stylistic style accommodation in a way that is agnostic to which specific speech style features will shift in a way that resembles socially motivated stylistic variation. This greatly expands the applicability of the model across contexts. Our hypothesis is that stylistic shifts that occur as a result of social processes are likely to display some consistency over time, and if we leverage this insight in our model, we will achieve a model that better captures inherent structure within speech.