Mitigating Data Imbalance in Automated Speaking Assessment

• URL: http://arxiv.org/abs/2509.03010v1
• Date: Wed, 03 Sep 2025 04:38:13 GMT
• Title: Mitigating Data Imbalance in Automated Speaking Assessment
• Authors: Fong-Chun Tsai, Kuan-Tang Huang, Bi-Cheng Yan, Tien-Hong Lo, Berlin Chen,
• Abstract summary: We introduce a novel objective for training ASA models, dubbed the Balancing Logit Variation (BLV) loss.<n>We show that integrating the BLV loss into a celebrated text-based (BERT) model significantly enhances classification accuracy and fairness.
• Score: 15.293800869580151
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automated Speaking Assessment (ASA) plays a crucial role in evaluating second-language (L2) learners proficiency. However, ASA models often suffer from class imbalance, leading to biased predictions. To address this, we introduce a novel objective for training ASA models, dubbed the Balancing Logit Variation (BLV) loss, which perturbs model predictions to improve feature representation for minority classes without modifying the dataset. Evaluations on the ICNALE benchmark dataset show that integrating the BLV loss into a celebrated text-based (BERT) model significantly enhances classification accuracy and fairness, making automated speech evaluation more robust for diverse learners.

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