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Large audio language models (LALMs) integrate audio representations with large language models to enable unified understanding of spoken content. Their capabilities have been increasingly investigated across several benchmarks; however, the examination of their use in rating L2 speech is still in its infancy. This study explores the abilities of LALMs in scoring three L2 speech global dimensions: foreign accentedness, comprehensibility, and intelligibility. Ninety audio samples produced by L2 speakers were rated by ten native speaker raters as well as five LALM models. Model performance was evaluated against the human composite mean using Pearson r, Spearman p, mean absolute error (MAE), and systematic bias, with the human leave-one-out correlation (r = .46-.73 across dimensions) serving as an empirical performance benchmark. The results showed that no LALM reached human-level performance on any dimension. Only one model (i.e., Gemini) achieved a significant correlation with human ratings on comprehensibility (r = .28, p &lt; .01), while Qwen2-Audio showed modest correlation on intelligibility (r = .32, p &lt; .01). MAE ranged from 0.75 to 3.99 for accentedness (human: 1.24), 1.35 to 3.00 for comprehensibility (human: 1.24), and 12.03 to 15.43 for intelligibility (human: 8.49). All models exhibited systematic biases, with deviations ranging from -9.31 to +13.19 points. The paper concludes with a discussion of the implications for automated L2 speech assessment.

ACL 2026

Assessment of L2 speech global dimensions using large audio language models

comprehensibility

lalms

intelligibility

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The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024) will take place in Bangkok, Thailand from August 11th to 16th, 2024. More information will be announced soon.

Large audio language models (LALMs) integrate audio representations with large language models to enable unified understanding of spoken content. Their capabilities have been increasingly investigated across several benchmarks; however, the examination of their use in rating L2 speech is still in its infancy. This study explores the abilities of LALMs in scoring three L2 speech global dimensions: foreign accentedness, comprehensibility, and intelligibility. Ninety audio samples produced by L2 speakers were rated by ten native speaker raters as well as five LALM models. Model performance was evaluated against the human composite mean using Pearson r, Spearman p, mean absolute error (MAE), and systematic bias, with the human leave-one-out correlation (r = .46-.73 across dimensions) serving as an empirical performance benchmark. The results showed that no LALM reached human-level performance on any dimension. Only one model (i.e., Gemini) achieved a significant correlation with human ratings on comprehensibility (r = .28, p < .01), while Qwen2-Audio showed modest correlation on intelligibility (r = .32, p < .01). MAE ranged from 0.75 to 3.99 for accentedness (human: 1.24), 1.35 to 3.00 for comprehensibility (human: 1.24), and 12.03 to 15.43 for intelligibility (human: 8.49). All models exhibited systematic biases, with deviations ranging from -9.31 to +13.19 points. The paper concludes with a discussion of the implications for automated L2 speech assessment.

workshop paper

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Large Language Models (LLMs) are widely applied in educational practices, such as for generating children's stories. However, the generated stories are often too difficult for children to read, and the operational cost of LLMs hinders their widespread adoption in educational settings. We used an existing expert-designed children's reading curriculum and its corresponding generated stories from GPT-4o and Llama 3.3 70B to design different experiments for fine-tuning three 8B-parameter LLMs, which then generated new English reading stories that were subjected to quantitative and qualitative evaluation. Our method prioritizes controllability over scale, enabling educators to target reading levels and error patterns with a compact, affordable model. Our evaluation results show that with appropriate fine-tuning designs, children's English reading stories generated by 8B LLMs perform better on difficulty-related metrics than those from GPT-4o and Llama 3.3 70B, with almost no discernible safety issues. Such fine-tuned LLMs could be more broadly used by teachers, parents, and children in classrooms and at home to generate engaging English reading stories with children's interests, controllable difficulty and safety.

Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety

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Children's English Reading Story Generation via Supervised Fine-Tuning of Compact LLMs with Controllable Difficulty and Safety

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