Feature selection, L1 vs. L2 regularization, and rotational invariance
ICML '04 Proceedings of the twenty-first international conference on Machine learning
International Journal of Human-Computer Studies - Special issue: Subtle expressivity for characters and robots
Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship
Wired for Speech: How Voice Activates and Advances the Human-Computer Relationship
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
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
A game-theoretic model of metaphorical bargaining
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Which clustering do you want? inducing your ideal clustering with minimal feedback
Journal of Artificial Intelligence Research
CMCL '11 Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics
Proceedings of the 15th ACM on International conference on multimodal interaction
Computer Speech and Language
Exploiting Psychological Factors for Interaction Style Recognition in Spoken Conversation
IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP)
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Automatically extracting social meaning and intention from spoken dialogue is an important task for dialogue systems and social computing. We describe a system for detecting elements of interactional style: whether a speaker is awkward, friendly, or flirtatious. We create and use a new spoken corpus of 991 4-minute speed-dates. Participants rated their interlocutors for these elements of style. Using rich dialogue, lexical, and prosodic features, we are able to detect flirtatious, awkward, and friendly styles in noisy natural conversational data with up to 75% accuracy, compared to a 50% baseline. We describe simple ways to extract relatively rich dialogue features, and analyze which features performed similarly for men and women and which were gender-specific.