Speech Representation Learning

Speech Representation Learning refers to the extraction of high-level abstract feature representations from speech signals using deep learning techniques, aiming to capture the intrinsic structure and semantic information of speech. Its goal is to build efficient and robust speech feature representation models to support downstream tasks such as speech recognition, emotion analysis, and speaker identification. Research in this field not only enhances the performance of speech processing systems but also promotes the development of multimodal fusion and cross-lingual transfer learning, making it highly valuable for practical applications.