"sure, I Did the Right Thing": a System for Sarcasm Detection in Speech

While a fair amount of work has been done on automatically detecting emotion in human speech, there has been little research on sarcasm detection. Although sarcastic speech acts are inherently subjective, humans have relatively clear intuitions as to what constitutes sarcastic speech. In this paper, we present a system for automatic sarcasm detection. Using a new acted speech corpus that is annotated for sarcastic and sincere speech, we examine a number of features that are indicative of sarcasm. The first set of features looks at a baseline of basic acoustic features that have been found to be helpful in human sarcasm identification. We then present an effective way of modeling and applying prosodic contours to the task of automatic sarcasm detection. This approach applies sequential modeling to categorical representations of pitch and intensity contours obtained via k-means clustering. Using a SimpleLogistic (LogitBoost) classifier, we are able to predict sarcasm with 81.57% accuracy. This result suggests that certain pitch and intensity contours are predictive of sarcastic speech.