Roadmap towards Superhuman Speech Understanding using Large Language Models
Fan Bu, Yuhao Zhang, Xidong Wang, Benyou Wang, Qun Liu, Haizhou Li
2024-10-18
Summary
This paper outlines a plan for improving speech understanding in AI using large language models (LLMs), ultimately aiming to create superhuman models that can understand and process both speech and audio data effectively.
What's the problem?
While large language models have made great progress in understanding text, they struggle with speech and audio data. Current systems often rely on basic speech recognition, which limits their ability to understand more complex aspects of communication, like tone or emotion. This gap means that AI can't fully grasp the richness of human speech, making it less effective in real-world applications.
What's the solution?
To tackle this issue, the authors propose a five-level roadmap for developing advanced speech LLMs. This roadmap starts from basic automatic speech recognition (ASR) and progresses to superhuman models that can integrate complex information from speech and audio. They also introduce the SAGI Benchmark, which helps evaluate these models across different levels by focusing on their ability to handle challenging tasks and abstract acoustic knowledge. This benchmark aims to identify gaps in current technology and guide future improvements.
Why it matters?
This research is important because it sets a clear direction for enhancing how AI understands speech, which is crucial for applications like virtual assistants, customer service bots, and more. By aiming for superhuman capabilities in speech understanding, this work could lead to AI systems that communicate more naturally with humans, improving interactions across various fields.
Abstract
The success of large language models (LLMs) has prompted efforts to integrate speech and audio data, aiming to create general foundation models capable of processing both textual and non-textual inputs. Recent advances, such as GPT-4o, highlight the potential for end-to-end speech LLMs, which preserves non-semantic information and world knowledge for deeper speech understanding. To guide the development of speech LLMs, we propose a five-level roadmap, ranging from basic automatic speech recognition (ASR) to advanced superhuman models capable of integrating non-semantic information with abstract acoustic knowledge for complex tasks. Moreover, we design a benchmark, SAGI Bechmark, that standardizes critical aspects across various tasks in these five levels, uncovering challenges in using abstract acoustic knowledge and completeness of capability. Our findings reveal gaps in handling paralinguistic cues and abstract acoustic knowledge, and we offer future directions. This paper outlines a roadmap for advancing speech LLMs, introduces a benchmark for evaluation, and provides key insights into their current limitations and potential.