mlsanityData to 7 Oct 2026

Exploring the Potential of Large Multimodal Models as Effective Alternatives for Pronunciation Assessment

arXiv 2503.11229 ↗First posted 14 Mar 2025cs.SD cs.CL eess.AScited by 4

On the map: Multimodal LLMs / vision-language models → MLLM training and visual instruction tuning

Abstract (arXiv)

Large Multimodal Models (LMMs) have demonstrated exceptional performance across a wide range of domains. This paper explores their potential in pronunciation assessment tasks, with a particular focus on evaluating the capabilities of the Generative Pre-trained Transformer (GPT) model, specifically GPT-4o. Our study investigates its ability to process speech and audio for pronunciation assessment across multiple levels of granularity and dimensions, with an emphasis on feedback generation and scoring. For our experiments, we use the publicly available Speechocean762 dataset. The evaluation focuses on two key aspects: multi-level scoring and the practicality of the generated feedback. Scoring results are compared against the manual scores provided in the Speechocean762 dataset, while feedback quality is assessed using Large Language Models (LLMs). The findings highlight the effectiveness of integrating LMMs with traditional methods for pronunciation assessment, offering insights into the model's strengths and identifying areas for further improvement.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in MLLM training and visual instruction tuning, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 14 of its 39 references that are in the map. First posted on arXiv 14 Mar 2025.

Builds on

14 of 39 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Pixtral 12B9 Oct
  2. 2024Pronunciation Assessment with Multi-modal Large Language Models12 Jul

Other references · 12

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2023GPT-4 Technical Reportcited by 18,132
  3. 2024The Llama 3 Herd of Modelscited by 14,192
  4. 2024Qwen2.5 Technical Reportcited by 4,713
  5. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  6. 2021Zero-Shot Text-to-Image Generationcited by 4,276
  7. 2023Neural Codec Language Models are Zero-Shot Text to Speech Synthesizerscited by 737
  8. 2024DeepSeek-V3 Technical Reportcited by 724
  9. 2024Phi-4 Technical Reportcited by 675
  10. 2024Human-AI Collaborative Essay Scoring: A Dual-Process Framework with LLMscited by 38
  11. 2023LLM-as-a-tutor in EFL Writing Education: Focusing on Evaluation of Student-LLM Interactioncited by 28
  12. 2024Can GPT-4 do L2 analytic assessment?cited by 16

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2026Zero-Shot Speech LLMs for Multi-Aspect Evaluation of L2 Speech: Challenges and Opportunitiescited by 2
  2. 2025Fine-Tuning Large Multimodal Models for Automatic Pronunciation Assessmentcited by 1
  3. 2026A Finetuned SpeechLLM for Joint Multi-Granular L2 Assessment and Natural-Language Rationalescited by 1
  4. 2026Prior over Evidence: Stereotype-Driven Diagnosis in LLM-Based L2 Pronunciation Feedbackcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025English Pronunciation Evaluation without Complex Joint Training: LoRA Fine-tuned Speech Multimodal LLM
  2. 2025Assessment of L2 Oral Proficiency using Speech Large Language Models
  3. 2025JCAPT: A Joint Modeling Approach for CAPT
  4. 2025Scaling Auditory Cognition via Test-Time Compute in Audio Language Models
  5. 2026A Benchmark for Audio Reasoning Capabilities of Multimodal Large Language Models
  6. 2026Alignment Is All You Need: Instruction-Free Training for General Audio-Language Models
  7. 2025Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis
  8. 2024Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency