# Spoken Grammar Assessment Using LLM

arXiv: https://arxiv.org/abs/2410.01579 · HTML: https://mlsanity.com/paper/2410.01579/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 2 Oct 2024.

## Builds on (most cited first, 3 of 3)

- [End-to-End Speech Recognition: A Survey](https://mlsanity.com/paper/2303.03329/) (2023)
- [Towards End-to-End Spoken Grammatical Error Correction](https://mlsanity.com/paper/2311.05550/) (2023)
- [Speaker Fluency Level Classification Using Machine Learning Techniques](https://mlsanity.com/paper/1808.10556/) (2018)

## Abstract (arXiv)

Spoken language assessment (SLA) systems restrict themselves to evaluating the pronunciation and oral fluency of a speaker by analysing the read and spontaneous spoken utterances respectively. The assessment of language grammar or vocabulary is relegated to written language assessment (WLA) systems. Most WLA systems present a set of sentences from a curated finite-size database of sentences thereby making it possible to anticipate the test questions and train oneself. In this paper, we propose a novel end-to-end SLA system to assess language grammar from spoken utterances thus making WLA systems redundant; additionally, we make the assessment largely unteachable by employing a large language model (LLM) to bring in variations in the test. We further demonstrate that a hybrid automatic speech recognition (ASR) with a custom-built language model outperforms the state-of-the-art ASR engine for spoken grammar assessment.