Vocal communication of emotion: a review of research paradigms
Speech Communication - Special issue on speech and emotion
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
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
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
Using linguistic cues for the automatic recognition of personality in conversation and text
Journal of Artificial Intelligence Research
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
Turn-taking cues in task-oriented dialogue
Computer Speech and Language
Proceedings of the 15th ACM on International conference on multimodal interaction
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Automatically detecting human social intentions and attitudes from spoken conversation is an important task for speech processing and social computing. We describe a system for detecting interpersonal stance: whether a speaker is flirtatious, friendly, awkward, or assertive. We make use of a new spoken corpus of over 1000 4-min speed-dates. Participants rated themselves and their interlocutors for these interpersonal stances, allowing us to build detectors for style both as interpreted by the speaker and as perceived by the hearer. We use lexical, prosodic, and dialog features in an SVM classifier to detect very clear styles (the strongest 10% in each stance) with up to 75% accuracy on previously seen speakers (50% baseline) and up to 59% accuracy on new speakers (48% baseline). A feature analysis suggests that flirtation is marked by joint focus on the woman as a target of the conversation, awkwardness by decreased speaker involvement, and friendliness by a conversational style including other-directed laughter and appreciations. Our work has implications for our understanding of interpersonal stance, their linguistic expression, and their automatic extraction.