Maec: Multi-Instance Learning With An Adversarial Auto-Encoder-Based Classifier For Speech Emotion Recognition

In this paper, we propose an adversarial auto-encoder-based classifier, which can regularize the distribution of latent representation to smooth the boundaries among categories. Moreover, we adopt multi-instance learning by dividing speech into a bag of segments to capture the most salient moments for presenting an emotion. The proposed model was trained on the IEMOCAP dataset and evaluated on the in-corpus validation set (IEMOCAP) and the cross-corpus validation set (MELD). The experiment results show that our model outperforms the baseline on in-corpus validation and increases the scores on cross-corpus validation with regularization.