Speech Text Data

Speech text data research focuses on bridging the gap between spoken and written language, primarily aiming to improve the accuracy and efficiency of tasks like speech recognition, translation, and understanding. Current research emphasizes leveraging large language models and self-supervised learning techniques, often incorporating contrastive learning or utilizing discrete units as intermediate targets to enhance model performance, even with limited paired speech-text data. These advancements are significant because they enable more robust and versatile spoken language processing systems, impacting applications ranging from virtual assistants to accessibility tools for individuals with speech impairments.