Assessment of L2 Oral Proficiency using Speech Large Language Models

• URL: http://arxiv.org/abs/2505.21148v1
• Date: Tue, 27 May 2025 12:58:21 GMT
• Title: Assessment of L2 Oral Proficiency using Speech Large Language Models
• Authors: Rao Ma, Mengjie Qian, Siyuan Tang, Stefano Bannò, Kate M. Knill, Mark J. F. Gales,
• Abstract summary: The growing population of L2 English speakers has increased the demand for developing automatic graders for spoken language assessment.<n>With the recent advancements of multi-modal large language models (LLMs), we aim to explore their potential as L2 oral proficiency graders.
• Score: 32.53590403242704
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The growing population of L2 English speakers has increased the demand for developing automatic graders for spoken language assessment (SLA). Historically, statistical models, text encoders, and self-supervised speech models have been utilised for this task. However, cascaded systems suffer from the loss of information, while E2E graders also have limitations. With the recent advancements of multi-modal large language models (LLMs), we aim to explore their potential as L2 oral proficiency graders and overcome these issues. In this work, we compare various training strategies using regression and classification targets. Our results show that speech LLMs outperform all previous competitive baselines, achieving superior performance on two datasets. Furthermore, the trained grader demonstrates strong generalisation capabilities in the cross-part or cross-task evaluation, facilitated by the audio understanding knowledge acquired during LLM pre-training.

Related papers

• Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis [4.774607166378613]
  Self-supervised pre-supervised training, combined with large language models (LLM), can effectively enhance ASR performance in low-resource scenarios.<n>We pre-train a Data2vec2 model on 300,000 hours of unlabeled dialect and accented speech data and do alignment training on a supervised dataset of 40,000 hours.
  arXiv  Detail & Related papers  (2025-05-27T12:50:55Z)
• Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models [79.90523648823522]
  Multi-stage continual learning can lead to catastrophic forgetting.<n>This paper evaluates three mitigation strategies-model merging, discounting the LoRA scaling factor, and experience replay.<n>Results show that experience replay is the most effective, with further gains achieved by combining it with other methods.
  arXiv  Detail & Related papers  (2025-05-23T05:50:14Z)
• Automatic Proficiency Assessment in L2 English Learners [51.652753736780205]
  Second language proficiency (L2) in English is usually perceptually evaluated by English teachers or expert evaluators.<n>This paper explores deep learning techniques for comprehensive L2 proficiency assessment, addressing both the speech signal and its correspondent transcription.
  arXiv  Detail & Related papers  (2025-05-05T12:36:03Z)
• Supervised Knowledge Makes Large Language Models Better In-context Learners [94.89301696512776]
  Large Language Models (LLMs) exhibit emerging in-context learning abilities through prompt engineering. The challenge of improving the generalizability and factuality of LLMs in natural language understanding and question answering remains under-explored. We propose a framework that enhances the reliability of LLMs as it: 1) generalizes out-of-distribution data, 2) elucidates how LLMs benefit from discriminative models, and 3) minimizes hallucinations in generative tasks.
  arXiv  Detail & Related papers  (2023-12-26T07:24:46Z)
• YAYI 2: Multilingual Open-Source Large Language Models [53.92832054643197]
  We propose YAYI 2, including both base and chat models, with 30 billion parameters. YAYI 2 is pre-trained from scratch on a multilingual corpus which contains 2.65 trillion tokens filtered by our pre-training data processing pipeline. The base model is aligned with human values through supervised fine-tuning with millions of instructions and reinforcement learning from human feedback.
  arXiv  Detail & Related papers  (2023-12-22T17:34:47Z)
• Tokenizer Choice For LLM Training: Negligible or Crucial? [30.33170936148845]
  We study the influence of tokenizer choice on Large Language Models (LLMs) downstream performance by training 24 mono- and multilingual LLMs. We find that the tokenizer choice can significantly impact the model's downstream performance and training costs. We show that multilingual tokenizers trained on the five most frequent European languages require vocabulary size increases of factor three in comparison to English.
  arXiv  Detail & Related papers  (2023-10-12T22:44:19Z)
• L2CEval: Evaluating Language-to-Code Generation Capabilities of Large Language Models [102.00201523306986]
  We present L2CEval, a systematic evaluation of the language-to-code generation capabilities of large language models (LLMs) We analyze the factors that potentially affect their performance, such as model size, pretraining data, instruction tuning, and different prompting methods. In addition to assessing model performance, we measure confidence calibration for the models and conduct human evaluations of the output programs.
  arXiv  Detail & Related papers  (2023-09-29T17:57:00Z)
• Mispronunciation detection using self-supervised speech representations [10.010024759851142]
  We study the use of SSL models for the task of mispronunciation detection for second language learners. We compare two downstream approaches: 1) training the model for phone recognition using native English data, and 2) training a model directly for the target task using non-native English data.
  arXiv  Detail & Related papers  (2023-07-30T21:20:58Z)
• The Interpreter Understands Your Meaning: End-to-end Spoken Language Understanding Aided by Speech Translation [13.352795145385645]
  Speech translation (ST) is a good means of pretraining speech models for end-to-end spoken language understanding. We show that our models reach higher performance over baselines on monolingual and multilingual intent classification. We also create new benchmark datasets for speech summarization and low-resource/zero-shot transfer from English to French or Spanish.
  arXiv  Detail & Related papers  (2023-05-16T17:53:03Z)
• A Survey of Large Language Models [81.06947636926638]
  Language modeling has been widely studied for language understanding and generation in the past two decades. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size.
  arXiv  Detail & Related papers  (2023-03-31T17:28:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.