Summary of Roadmap Towards Superhuman Speech Understanding Using Large Language Models, by Fan Bu et al.
Roadmap towards Superhuman Speech Understanding using Large Language Models
by Fan Bu, Yuhao Zhang, Xidong Wang, Benyou Wang, Qun Liu, Haizhou Li
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed five-level roadmap for developing end-to-end speech large language models (LLMs) aims to create general foundation models that can process both textual and non-textual inputs. The roadmap spans from basic automatic speech recognition (ASR) to advanced superhuman models capable of integrating abstract acoustic knowledge with non-semantic information for complex tasks. A benchmark, SAGI Bechmark, is designed to standardize critical aspects across various tasks in these five levels, uncovering challenges in using abstract acoustic knowledge and completeness of capability. The findings reveal gaps in handling paralinguistic cues and abstract acoustic knowledge, offering future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Speech models are getting better at understanding language! Researchers want to make them even more powerful by adding speech data. This will help machines understand speech better, like humans do. To get there, they created a roadmap with five levels: basic speech recognition to superhuman models that can combine audio clues with knowledge for complex tasks. They also made a test to see how well these models work and what’s missing. The results show where we need to improve, like understanding tone and pitch. |
