8 papers · 1 filter
Assessment of L2 Oral Proficiency using Speech Large Language Models
Rao Ma, Mengjie Qian, Siyuan Tang +3
The growing population of L2 English speakers has increased the demand for developing automatic graders for spoken language assessment (SLA). Historically, statistical models, text…
Scaling and Prompting for Improved End-to-End Spoken Grammatical Error Correction
Mengjie Qian, Rao Ma, Stefano Bannò +2
Spoken Grammatical Error Correction (SGEC) and Feedback (SGECF) are crucial for second language learners, teachers and test takers. Traditional SGEC systems rely on a cascaded pipe…
Speak & Improve Corpus 2025: an L2 English Speech Corpus for Language Assessment and Feedback
Kate Knill, Diane Nicholls, Mark J. F. Gales +2
We introduce the Speak & Improve Corpus 2025, a dataset of L2 learner English data with holistic scores and language error annotation, collected from open (spontaneous) speaking te…
Speak & Improve Challenge 2025: Tasks and Baseline Systems
Mengjie Qian, Kate Knill, Stefano Banno +4
This paper presents the "Speak & Improve Challenge 2025: Spoken Language Assessment and Feedback" -- a challenge associated with the ISCA SLaTE 2025 Workshop. The goal of the chall…
Zero-shot Audio Topic Reranking using Large Language Models
Mengjie Qian, Rao Ma, Adian Liusie +3
Multimodal Video Search by Examples (MVSE) investigates using video clips as the query term for information retrieval, rather than the more traditional text query. This enables far…
Grammatical Error Feedback: An Implicit Evaluation Approach
Stefano Bannò, Kate Knill, Mark J. F. Gales
Grammatical feedback is crucial for consolidating second language (L2) learning. Most research in computer-assisted language learning has focused on feedback through grammatical er…