Paralinguistic Feature

Paralinguistic features, encompassing aspects of speech beyond the literal words (e.g., tone, emotion, speaking style), are increasingly central to research in speech processing and understanding. Current efforts focus on integrating paralinguistic information into large language models (LLMs) using various techniques, including hierarchical feature fusion, contrastive learning, and multimodal architectures like transformer encoders, to improve tasks such as speech emotion recognition and spoken dialogue modeling. This research is significant for advancing human-computer interaction, improving the accuracy of speech-based health monitoring, and developing more nuanced and empathetic AI systems.