Cross-Domain Transfer Learning for Complex Emotion Recognition

Automatic recognition of basic emotions has improved the understanding of human emotions and has paved the path to understand complex emotions better, closer to real-world scenarios and having immediate applications. This paper investigates the utility of spectrogram (obtained from audio signals) images to perform complex emotion recognition by using transfer learning technique. Further, visual domain frameworks are adapted to audio frameworks. Transfer learning using pre-trained AlexNet are investigated. Information extracted from deep layers of the transfer learned networks was encoded as features and were trained on a linear classifier. Experimental results on EmoReact dataset consisting of 8 complex emotions shows the effectiveness of the proposed framework. Highest F1 score and AUC-ROC values as against the baseline performance by 16% and 10% respectively have been observed.