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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.01579 |
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| _version_ | 1866910628842569728 |
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| author | Kopparapu, Sunil Kumar Bhat, Chitralekha Panda, Ashish |
| author_facet | Kopparapu, Sunil Kumar Bhat, Chitralekha Panda, Ashish |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01579 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Spoken Grammar Assessment Using LLM Kopparapu, Sunil Kumar Bhat, Chitralekha Panda, Ashish Computation and Language Artificial Intelligence 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. |
| title | Spoken Grammar Assessment Using LLM |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2410.01579 |