Spoken Grammar Assessment Using LLM
<|reference_start|>Spoken Grammar Assessment Using LLM: 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.<|reference_end|>
@article{kopparapu2024spoken,
    title={Spoken Grammar Assessment Using LLM},
    author={Sunil Kumar Kopparapu and Chitralekha Bhat and Ashish Panda},
    journal={arXiv preprint arXiv:2410.01579},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01579},
    primaryClass={cs.CL cs.AI}
}