Exploring the Application of Large Language Models in Spoken Language Understanding Tasks
Mingjie Li · 2024
This paper explores the field of Spoken Language Understanding (SLU), a subdomain of Natural Language Processing (NLP). SLU focuses on the processing and comprehension of spoken or voice input, aiming to extract meaningful information from user utterances. A key component of SLU is Automatic Speech Recognition (ASR), which converts spoken language into textual form. The paper outlines the objectives and significance of SLU, emphasizing its ability to enable computers to comprehend and act upon spoken instructions. By integrating ASR and subsequent language understanding techniques, SLU systems facilitate the development of more intuitive and efficient human-computer interactions. The paper provides a brief overview of the key techniques and components involved in SLU, highlighting the importance of accurate speech recognition and effective language parsing in achieving the desired outcomes.