Spoken Grammar Assessment Using LLM

• URL: http://arxiv.org/abs/2410.01579v1
• Date: Wed, 2 Oct 2024 14:15:13 GMT
• Title: Spoken Grammar Assessment Using LLM
• Authors: Sunil Kumar Kopparapu, Chitralekha Bhat, Ashish Panda,
• Abstract summary: Spoken language assessment (SLA) systems analyse the pronunciation and oral fluency of a speaker by analysing the read and spontaneous spoken utterances respectively. 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. We propose a novel end-to-end SLA system to assess language grammar from spoken utterances thus making WLA systems redundant.
• Score: 10.761744330206065
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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