Detecting friendly, flirtatious, awkward, and assertive speech in speed-dates

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.