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Unlocking Large Audio-Language Models for Interactive Language Learning

Hongfu Liu, Zhouying Cui, Xiangming Gu, Ye Wang

TL;DR

The paper tackles improving chat-based pronunciation training for second-language learners by leveraging audio-language models (ALMs). It introduces L2-Arctic-plus, a dataset with ground-truth error explanations and actionable feedback, enabling robust evaluation of mispronunciation detection and feedback quality. Through a systematic comparison, cascaded ASR+LLMs struggle to capture pronunciation errors, while existing ALMs show promise but are limited without task-specific training. The authors propose instruction tuning of ALMs on L2-Arctic-plus, including a two-stage acoustic alignment and instruction-tuning pipeline, achieving substantial gains in detection and feedback quality, reducing hallucinations, and surpassing baselines in human evaluations. These findings demonstrate the value of end-to-end ALMs and curated datasets for interactive language learning, with potential for richer multimodal feedback in future work.

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.

Unlocking Large Audio-Language Models for Interactive Language Learning

TL;DR

The paper tackles improving chat-based pronunciation training for second-language learners by leveraging audio-language models (ALMs). It introduces L2-Arctic-plus, a dataset with ground-truth error explanations and actionable feedback, enabling robust evaluation of mispronunciation detection and feedback quality. Through a systematic comparison, cascaded ASR+LLMs struggle to capture pronunciation errors, while existing ALMs show promise but are limited without task-specific training. The authors propose instruction tuning of ALMs on L2-Arctic-plus, including a two-stage acoustic alignment and instruction-tuning pipeline, achieving substantial gains in detection and feedback quality, reducing hallucinations, and surpassing baselines in human evaluations. These findings demonstrate the value of end-to-end ALMs and curated datasets for interactive language learning, with potential for richer multimodal feedback in future work.

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.
Paper Structure (49 sections, 3 equations, 12 figures, 11 tables)

This paper contains 49 sections, 3 equations, 12 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Audio-Language Modeling.
  4. Computer-Assisted Pronunciation Training.
  5. Interactive Language Learning
  6. Problem Statement
  7. Dataset Curation of L2-Arctic-plus
  8. Evaluation Protocols
  9. Mispronunciation Detection Evaluation.
  10. Feedback Generation Evaluation.
  11. Investigating ASR+LLMs Cascade
  12. Cascaded ASR+LLM Framework
  13. ASR-based Transcription.
  14. LLM-based In-Context Learning.
  15. Evaluation Results
  16. ...and 34 more sections

Figures (12)

  • Figure 1: Illustrative examples of chat-based pronunciation training for interactive language learning. The system generates detection with error explanations and suggestions with practical corrective actions to provide more user-friendly feedback.
  • Figure 2: Overview of (left) ASR+LLMs cascade; (middle) existing ALMs; (right) instruction-tuning ALMs. For instruction-tuning ALMs, the upper right corner shows the trainable module in a two-stage pipeline. A represents the Audio Encoder, P represents the Projector, and L represents the Large Language Model.
  • Figure 3: Ground truth generation prompt (GPT-4o).
  • Figure 4: Ground truth generation example (GPT-4o).
  • Figure 5: Cascaded ASR+LLMs Prompt
  • ...and 7 more figures