Joint Speech and Text Training for LLM-Based End-to-End Spoken Dialogue State Tracking

This article likely presents a research paper exploring the use of Large Language Models (LLMs) for spoken dialogue state tracking. The focus is on training the LLM using both speech and text data, which is a common approach to improve performance in speech-related tasks. The title suggests an end-to-end approach, meaning the system likely processes the entire dialogue without intermediate steps. The source, ArXiv, indicates this is a pre-print, meaning it's a research paper that has not yet undergone peer review.

• •Focus on using LLMs for spoken dialogue state tracking.
• •Employs joint training with speech and text data.
• •Likely an end-to-end approach.
• •Published on ArXiv, indicating it's a pre-print.