Exploring the Potential of Large Multimodal Models as Effective Alternatives for Pronunciation Assessment
This work addresses pronunciation assessment for language learners, but it is incremental as it applies existing models to a new domain.
This paper explored using Large Multimodal Models, specifically GPT-4o, for pronunciation assessment tasks, finding them effective when integrated with traditional methods, with results compared to manual scores from the Speechocean762 dataset.
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.