We propose detecting and responding to humor in spoken dialogue by extracting language and audio cues and subsequently feeding these features into a combined recurrent neural network (RNN) and logistic regression model. In this paper, we parse Switchboard phone conversations to build a corpus of punchlines and unfunny lines where punchlines precede laughter tokens in Switchboard transcripts. We create a combined RNN and logistic regression model that uses both acoustic and language cues to predict whether a conversational agent should respond to an utterance with laughter. Our model achieves an F1-score of 63.2 and accuracy of 73.9. This model outperforms our logistic language model (F1-score 56.6) and RNN acoustic model (59.4) as well as the final RNN model of D. Bertero, 2016 (52.9). Using our final model, we create a “laughbot” that audibly responds to a user with laughter when their utterance is classified as a punchline. A conversational agent outfitted with a humor-recognition system such as the one we present in this paper would be valuable as these agents gain utility in everyday life.
CITATION STYLE
Park, K., Hu, A., & Muenster, N. (2019). Laughbot: Detecting humor in spoken language with language and audio cues. In Advances in Intelligent Systems and Computing (Vol. 886, pp. 644–656). Springer Verlag. https://doi.org/10.1007/978-3-030-03402-3_45
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