Grammatical feedback is crucial for L2 learners, teachers, and testers.
Spoken grammatical error correction (GEC) aims to supply feedback to L2
learners on their use of grammar when speaking. This process usually relies on
a cascaded pipeline comprising an ASR system, disfluency removal, and GEC, with
the associated concern of propagating errors between these individual modules.
In this paper, we introduce an alternative "end-to-end" approach to spoken GEC,
exploiting a speech recognition foundation model, Whisper. This foundation
model can be used to replace the whole framework or part of it, e.g., ASR and
disfluency removal. These end-to-end approaches are compared to more standard
cascaded approaches on the data obtained from a free-speaking spoken language
assessment test, Linguaskill. Results demonstrate that end-to-end spoken GEC is
possible within this architecture, but the lack of available data limits
current performance compared to a system using large quantities of text-based
GEC data. Conversely, end-to-end disfluency detection and removal, which is
easier for the attention-based Whisper to learn, does outperform cascaded
approaches. Additionally, the paper discusses the challenges of providing
feedback to candidates when using end-to-end systems for spoken GEC.

本文介绍了一种针对 L2 学习者的口语语法错误修正的替代性 “端到端” 方法，利用了语音识别基础模型 Whisper 进行整个框架或部分替换，结果表明这种端到端方法在体系结构内是可行的，但由于缺乏可用数据，当前性能有限。此外，文中还讨论了使用端到端系统进行口语语法错误修正时提供反馈的挑战。