It's not you, it's me: Detecting flirting and its misperception in speed-dates

Automatically detecting human social intentions from spoken conversation is an important task for dialogue understanding. Since the social intentions of the speaker may differ from what is perceived by the hearer, systems that analyze human conversations need to be able to extract both the perceived and the intended social meaning. We investigate this difference between intention and perception by using a spoken corpus of speed-dates in which both the speaker and the listener rated the speaker on flirtatiousness. Our flirtationdetection system uses prosodic, dialogue, and lexical features to detect a speaker's intent to flirt with up to 71.5% accuracy, significantly outperforming the baseline, but also outperforming the human interlocuters. Our system addresses lexical feature sparsity given the small amount of training data by using an autoencoder network to map sparse lexical feature vectors into 30 compressed features. Our analysis shows that humans are very poor perceivers of intended flirtatiousness, instead often projecting their own intended behavior onto their interlocutors.