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Deep CNN-based Inductive Transfer Learning for Sarcasm Detection in Speech

Sarcasm is a frequently used linguistic device which is ex-pressed in a multitude of ways, both with acoustic cues (includ-ing pitch, intonation, intensity, etc.) and visual cues (including facial expression, eye gaze, etc.). While cues used in the ex-pression of sarcasm are well-described in the literature, there is a striking paucity of attempts to perform automatic sarcasm detection in speech. To explore this gap, we elaborate a methodology of implementing Inductive Transfer Learning (ITL) based on pre-trained Deep Convolutional Neural Networks (DCNNs) to detect sarcasm in speech. To those ends, the multimodal dataset MUStARD is used as a target dataset in this study. The two selected pre-trained DCNN models used are Xception and VGGish, which we trained on visual and audio datasets. Results show that VGGish, which is applied as a feature extractor in the experiment, performs better than Xception, which has its convolutional layers and pooling layers retrained. Both models achieve a higher F-score compared to the baseline Support Vector Machines (SVM) model by 7% and 5% in unimodal sarcasm detection in speech.