6 accepted papers
Multimodal emotion recognition (MER) is important for building humanoid chatbots and has gained increasing attention in recent years. Existing studies have proven that extracting better modality-specific representations, which keep both commonality and individuality information of different modaliti…
Multi-modal emotion recognition (MER) using speech and text has attracted extensive attention because of the easy availability of data for these two modalities. Recently, the self-surprised learning (SSL) pre-trained model has become the state-of-the-art (SOTA) method for the extraction of acoustic…
Multimodal learning is susceptible to modality missing, which poses a major obstacle for its practical applications and, thus, invigorates increasing research interest. In this paper, we investigate two challenging problems: 1) when modality missing exists in the training data, how to exploit the in…
Multi-modal emotion recognition has gained increasing attention in recent years due to its widespread applications and the advances in multi-modal learning approaches. However, previous studies primarily focus on developing models that exploit the unification of multiple modalities. In this paper, w…
This paper proposes a new model, called hierarchical and multi-view dependency modelling network (HMVDM), for the task of emotion recognition in conversations (ERC). The modelling of conversational context plays an important role in ERC, especially for the multi-turn and multi-speaker conversations…
This paper proposes a new model, called condition-transforming variational autoencoder (CTVAE), to improve the performance of conversation response generation using conditional variational autoencoders (CVAEs). In conventional CVAEs , the prior distribution of latent variable z follows a multivariat…