Factorial Hidden Markov Models
Machine Learning - Special issue on learning with probabilistic representations
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
Extracting social meaning: identifying interactional style in spoken conversation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Social signal processing: Survey of an emerging domain
Image and Vision Computing
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
Opensmile: the munich versatile and fast open-source audio feature extractor
Proceedings of the international conference on Multimedia
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Multiple instance learning for classification of human behavior observations
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part II
Robust Voice Activity Detection Using Long-Term Signal Variability
IEEE Transactions on Audio, Speech, and Language Processing
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In human-human interactions, entrainment is a naturally occurring phenomenon that happens when interlocutors mutually adapt their behaviors through the course of an interaction. This mutual behavioral dependency has been at the center of psychological studies of human communication for decades. Quantitative descriptors of the degree of entrainment can provide psychologists an objective method to advance studies of human communication including in mental health domains. However, the subtle nature of the entrainment phenomenon makes it challenging for computing such an effect based on just human annotations. In this paper, we propose an unsupervised signal-derived approach within a principal component analysis framework for quantifying one aspect of entrainment in communication, namely, vocal entrainment. The proposed approach to quantify the degree of vocal entrainment involves measuring the similarity of specific vocal characteristics between the interlocutors in a dialog. These quantitative descriptors were analyzed using two psychology-inspired hypothesis tests to not only establish that these signal-derived measures carry meaningful information in interpersonal communication but also offer statistical evidence into aspects of behavioral dependency and associated affective states in marital conflictual interactions. Finally, affect recognition experiments were performed with the proposed vocal entrainment descriptors as features using a large database of real distressed married couples' interactions. An accuracy of 62.56% in differentiating between positive and negative affect was obtained using these entrainment measures with Factorial Hidden Markov Models lending further support that entrainment is an active component underlying affective processes in interactions.