A Novel Data Augmentation Approach for Automatic Speaking Assessment on Opinion Expressions

Automated speaking assessment (ASA) on opinion expressions is often hampered by the scarcity of labeled recordings, which restricts prompt diversity and undermines scoring reliability. To address this challenge, we propose a novel training paradigm that leverages a large language models (LLM) to generate diverse responses of a given proficiency level, converts responses into synthesized speech via speaker-aware text-to-speech synthesis, and employs a dynamic importance loss to adaptively reweight training instances based on feature distribution differences between synthesized and real speech. Subsequently, a multimodal large language model integrates aligned textual features with speech signals to predict proficiency scores directly. Experiments conducted on the L TTC dataset show that our approach outperforms methods relying on real data or conventional augmentation, effectively mitigating low-resource constraints and enabling ASA on opinion expressions with cross-modal information. Index T erms: Automated speaking assessment, Opinion Expression, Data Augmentation 1. Introduction In recent years, technologies for computer-assisted language learning (CALL), such as automated speaking assessment (ASA), have made significant strides to meet the growing demand for scalable and objective evaluation of second-language (L2) speaking proficiency in both academic and professional contexts [1, 2, 3].