4 accepted papers
Large language model (LLM)-based visual dialogue (VD) systems have made response generation for image-grounded conversations more correct and coherent. However, user engagement - the extent to which a user is interested, emotionally involved, and willing to continue the conversation - remains a chal
Multimodal Emotion Recognition in Conversations (ERC) plays a crucial role in understanding human language and behavior in real-world scenarios. However, existing research tends to simply concatenate multimodal representations, failing to capture the complex relationships between modalities. Recent…
The Pre-trained Speech Models (PSMs) generate universal speech representations using self-supervised or weakly-supervised learning from large-scale datasets. It achieves promising performance when fine-tuned for specific tasks such as Speech Emotion Recognition (SER). However, fine-tuning on various…
Graph representation learning has become a hot research topic due to its powerful nonlinear fitting capability in extracting representative node embeddings. However, for sequential data such as speech signals, most traditional methods merely focus on the static graph created within a sequence, and l…