Year: 2026
Type: conference-paper
Abstract: Achieving pronunciation proficiency in a second language (L2) remains a challenge, despite the development of Computer-Assisted Pronunciation Training (CAPT) systems.Traditional CAPT systems often provide unintuitive feedback that lacks actionable guidance, limiting its effectiveness.Recent advancements in audiolanguage models (ALMs) offer the potential to enhance these systems by providing more userfriendly feedback.In this work, we investigate ALMs for chat-based pronunciation training by introducing L2-Arctic-plus, an English dataset with detailed error explanations and actionable suggestions for improvement.We benchmark cascaded ASR+LLMs and existing ALMs on this dataset, specifically in detecting mispronunciation and generating actionable feedback.To improve the performance, we further propose to instruction-tune ALMs on L2-Arcticplus.Experimental results demonstrate that our instruction-tuned models significantly outperform existing baselines on mispronunciation detection and suggestion generation in terms of both objective and human evaluation, highlighting the value of the proposed dataset 1 .
Authors: Hongfu Liu, Zhouying Cui, Xiangming Gu, Ye Wang
Institution: National University of Singapore
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
Funder: Ministry of Education, India
OpenAlex ID: W7140112366