Summary of Spoken Grammar Assessment Using Llm, by Sunil Kumar Kopparapu and Chitralekha Bhat and Ashish Panda
Spoken Grammar Assessment Using LLM
by Sunil Kumar Kopparapu, Chitralekha Bhat, Ashish Panda
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel end-to-end Spoken Language Assessment (SLA) system is proposed to assess language grammar from spoken utterances, making Written Language Assessment (WLA) systems redundant. The SLA system employs a large language model (LLM) to introduce variations in the test, making it largely unteachable. A hybrid automatic speech recognition (ASR) engine with a custom-built language model outperforms state-of-the-art ASR engines for spoken grammar assessment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to check someone’s language skills by listening to them speak. Currently, there are two kinds of tests: one that checks how well you can write and one that checks how well you can talk. The talking test only looks at how good your pronunciation is, not what you’re actually saying. This new test can also look at the grammar and vocabulary in what someone says, making the writing test unnecessary. |
Keywords
» Artificial intelligence » Language model » Large language model
