Non Semantic Speech Task

Non-semantic speech tasks focus on extracting information from speech beyond its literal meaning, encompassing areas like emotion recognition, speaker identification, and disfluency detection. Current research emphasizes leveraging pre-trained models, particularly transformer architectures and self-supervised learning techniques, often combined with active learning strategies to improve efficiency and reduce data requirements. These advancements are driving progress in building more robust and resource-efficient systems for applications ranging from improved human-computer interaction to healthcare diagnostics and personalized assistive technologies.