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arXiv · 2601.14744

Unlocking Large Audio-Language Models for Interactive Language Learning

Hongfu Liu·Zhouying Cui·Xiangming Gu·Ye Wang
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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 audio-language models (ALMs) offer the potential to enhance these systems by providing more user-friendly 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-Arctic-plus. 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.

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Hongfu Liu, Zhouying Cui, Xiangming Gu, Ye Wang. 2026-01-21. Unlocking Large Audio-Language Models for Interactive Language Learning. https://arxiv.org/abs/2601.14744

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