用大模型实现口语语法自动评分，让应试训练失效。
Spoken Grammar Assessment Using LLM
- 用大语言模型生成动态考题，避免题目可预测。
- 混合语音识别模型在口语语法评估中超越现有最优方案。
- 首个端到端口语语法评分系统，适合语言教学研究者。
口语语言评估（SLA）系统通常仅分析朗读或即兴口语中的发音和流利度，语法与词汇评估仍由书面语言评估（WLA）系统承担。多数WLA系统依赖预设有限语料库生成题目，导致考生可提前准备。本文提出一种新型端到端口语语法评估系统，直接从口语中判断语法正确性，使传统WLA系统过时；同时利用大语言模型引入测试多样性，大幅降低应试训练效果。实验表明，结合自建语言模型的混合自动语音识别（ASR）系统，在口语语法评估任务上优于当前最先进的ASR引擎。
原文摘要 · Abstract (English)
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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