3 accepted papers
The ability to handle various emotion labels without dedicated training is crucial for building adaptable Emotion Recognition (ER) systems. Conventional ER models rely on training using fixed label sets and struggle to generalize beyond them. On the other hand, Large Language Models (LLMs) have show…
Self-supervised pretraining has transformed speech representation learning, enabling models to generalize across various downstream tasks. However, empirical studies have highlighted two notable gaps. First, different speech tasks require varying levels of acoustic and semantic information, which ar…
Humans’ internal states play a key role in human-machine interaction, leading to the rise of human state estimation as a prominent field. Compared to swift state changes such as surprise and irritation, modeling gradual states like trust and satisfaction are further challenged by label sparsity: lon…